{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Depth Data Analysis for DynSLAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import os\n",
    "import sys\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from matplotlib import rc\n",
    "# Enable full LaTeX support in plot text. Requires a full-fledged LaTeX installation\n",
    "# on your system, accessible via PATH.\n",
    "rc('text', usetex=True)\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (16, 6)\n",
    "matplotlib.rcParams.update({'font.size': 16})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kitti-odometry-06-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-dynamic-mode-NO-direct-ref-depth-result.csv\n",
      "kitti-odometry-06-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-dynamic-mode-NO-direct-ref-3d-tracking-result.csv\n",
      "kitti-odometry-08-offset-3969-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-dynamic-mode-NO-direct-ref-depth-result.csv\n",
      "kitti-odometry-08-offset-3969-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-dynamic-mode-NO-direct-ref-3d-tracking-result.csv\n",
      "kitti-odometry-08-offset-3969-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-NO-direct-ref-depth-result.csv\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "(cd ../cmake-build-debug && (ls -t *.csv | head -n 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UNFAIR COMPARISON!!!\n"
     ]
    }
   ],
   "source": [
    "# CSV_FILE_DISPNET = \"../cmake-build-debug/kitti-odometry-06-offset-0-depth-precomputed-dispnet-voxelsize-0.0350-results.csv\"\n",
    "# CSV_FILE_ELAS = \"../cmake-build-debug/kitti-odometry-08-offset-3969-results.csv\"\n",
    "# CSV_FILE_ELAS = \"../cmake-build-debug/kitti-odometry-06-offset-0-depth-precomputed-elas-voxelsize-0.0350-results.csv\"\n",
    "\n",
    "# CSV_FILE_DISPNET=\"../cmake-build-debug/kitti-odometry-05-offset-0-depth-precomputed-dispnet-voxelsize-0.0350-results.csv\"\n",
    "# CSV_FILE_ELAS=\"../cmake-build-debug/kitti-odometry-05-offset-0-depth-precomputed-elas-voxelsize-0.0350-results.csv\"\n",
    "\n",
    "# CSV_FILE_DISPNET=\"../cmake-build-debug/kitti-odometry-06-offset-0-depth-precomputed-dispnet-voxelsize-0.0350-results.csv\"\n",
    "# CSV_FILE_ELAS=\"../cmake-build-debug/kitti-odometry-06-offset-0-depth-precomputed-elas-voxelsize-0.0350-results.csv\"\n",
    "\n",
    "# CSV_FILE_DISPNET=\"../cmake-build-debug/kitti-odometry-03-offset-500-depth-precomputed-dispnet-voxelsize-0.0350-max-depth-m-12.00-results.csv\"\n",
    "# CSV_FILE_DISPNET=\"../cmake-build-debug/kitti-odometry-03-offset-500-depth-precomputed-elas-voxelsize-0.0350-max-depth-m-12.00-results.csv\"\n",
    "\n",
    "# Same as above but with 16m max depth\n",
    "# CSV_FILE_DISPNET=\"../cmake-build-debug/kitti-odometry-03-offset-500-depth-precomputed-dispnet-voxelsize-0.0350-max-depth-m-16.00-results.csv\"\n",
    "# CSV_FILE_ELAS =\"../cmake-build-debug/kitti-odometry-03-offset-500-depth-precomputed-elas-voxelsize-0.0350-max-depth-m-16.00-results.csv\"\n",
    "\n",
    "CSV_FILE_DISPNET = \"../cmake-build-debug/kitti-odometry-06-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-results.csv\"\n",
    "CSV_FILE_ELAS = \"../cmake-build-debug/kitti-odometry-06-offset-0-depth-precomputed-elas-voxelsize-0.0500-max-depth-m-20.00-results.csv\"\n",
    "\n",
    "# just trying to see exactly how much better the improvement is\n",
    "# CSV_FILE_ELAS = \"../cmake-build-debug/kitti-tracking-sequence-0004-tracking-dataset-offset-0-depth-precomputed-elas-voxelsize-0.0500-max-depth-m-20.00-results.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'CSV_FILE_DISPNET' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m--------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-5ecffa4f711f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mframe_dn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCSV_FILE_DISPNET\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mframe_elas\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCSV_FILE_ELAS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mDELTA_MAX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe_dn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe_elas\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'CSV_FILE_DISPNET' is not defined"
     ]
    }
   ],
   "source": [
    "frame_dn = pd.read_csv(CSV_FILE_DISPNET)\n",
    "frame_elas = pd.read_csv(CSV_FILE_ELAS)\n",
    "DELTA_MAX = 8\n",
    "\n",
    "len(frame_dn), len(frame_elas)\n",
    "\n",
    "def augment(dframe):\n",
    "    for name in ['input', 'fusion']:\n",
    "        for delta in range(1, DELTA_MAX+1):\n",
    "            total = dframe['{}-total-{:.2f}'.format(name, delta)]\n",
    "            error = dframe['{}-error-{:.2f}'.format(name, delta)]\n",
    "            missing = dframe['{}-missing-{:.2f}'.format(name, delta)]\n",
    "            correct = dframe['{}-correct-{:.2f}'.format(name, delta)]\n",
    "\n",
    "            dframe['{}-error-percent-{:.2f}'.format(name, delta)] = error / total\n",
    "            dframe['{}-error-and-missing-percent-{:.2f}'.format(name, delta)] = (error+missing) / total\n",
    "            dframe['{}-missing-percent-{:.2f}'.format(name, delta)] = missing / total\n",
    "            dframe['{}-correct-percent-{:.2f}'.format(name, delta)] = correct / total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "augment(frame_dn)\n",
    "augment(frame_elas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Timeseries Comparison Plots\n",
    "\n",
    "These could show how the error% for different configs changes over time, in the same plot (cf. the stacked \"Timeseries plots\" below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def timeseries_comp(df, missing_are_errors):\n",
    "    ax = None\n",
    "    for i in [1, 3, 8]:\n",
    "        in_lbl = 'Input error for $\\delta_{{max}} = {}$'.format(i)\n",
    "        in_key = 'input-{}-percent-{}'.format('error-and-missing' if missing_are_errors else 'error', i)\n",
    "        ax = df[in_key].plot(label=in_lbl)\n",
    "        \n",
    "        fus_lbl = 'Fusion error for $\\delta_{{max}} = {}$'.format(i)\n",
    "        fus_key = 'fusion-{}-percent-{}'.format('error-and-missing' if missing_are_errors else 'error', i)\n",
    "        ax = df[fus_key].plot(label=fus_lbl, marker='x')\n",
    "\n",
    "    return ax\n",
    "\n",
    "ax = timeseries_comp(frame_elas, False)\n",
    "ax.set_ylim([0,1])\n",
    "ax.set_ylabel(\"Percentage of error pixels\")\n",
    "ax.set_xlabel(\"Frame\")\n",
    "ax.legend()\n",
    "\n",
    "# ax = timeseries_comp(frame_elas, True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boxplots\n",
    "\n",
    "We could use these plots to compare the error distributions for different scenes and different parameter configurations; for instance, we could show that errors tend to be smaller for dynamic scenes when dynamic objects are accounted for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def boxplots(elas_frame, dn_frame, delta_max):\n",
    "    bp_df = pd.DataFrame({\n",
    "        'ELAS input errors': elas_frame['input-error-percent-{:.2f}'.format(delta_max)],\n",
    "        'Dispnet input errors': dn_frame['input-error-percent-{:.2f}'.format(delta_max)],\n",
    "        'ELAS fusion errors': elas_frame['fusion-error-percent-{:.2f}'.format(delta_max)],\n",
    "        'Dispnet fusion errors': dn_frame['fusion-error-percent-{:.2f}'.format(delta_max)]\n",
    "    })\n",
    "    ax = bp_df.boxplot()\n",
    "    ax.set_ylim([0, 0.5])\n",
    "    ax.set_ylabel(\"Percentage of incorrect pixels\")\n",
    "    ax.set_title(\"For sequence dumped to file {}\".format(CSV_FILE_ELAS))\n",
    "    ax.grid('off')\n",
    "    \n",
    "# Well... do note that in the LSD-SLAM paper they don't *really* show the outliers, do they?\n",
    "boxplots(frame_elas, frame_dn, delta_max=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspiration: the boxplots used for the point-to-plane distance based evaluation for LSD-SLAM:\n",
    "\n",
    "![](img/lsd-slam-results.png)\n",
    "\n",
    "Note how the libelas errors are actually smaller than their result fused over time. That's quite interesting...\n",
    "\n",
    "Also note that we can't rely too much on their exact evaluation technique, which aligns MULTIPLE velodyne pointclouds for generating the ground truth, since it wouldn't work in evaluating the accuracy of reconstructing dynamic objects.\n",
    "\n",
    "FROM THEIR PAPER: We give median, 5th, and 95th percentile on the various Kitti sequences. => No outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Timeseries Plots \n",
    "\n",
    "These can highlight how errors change over time. For dynamic sequences but static reconstruction, these could show error spikes when other moving cars enter view. Then, we could show how (most of) those spikes are mitigated once we become aware of the dynamic objects and start reconstructing them separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def timeplot(frame, delta=5):\n",
    "    time_colors = ['red', 'green', 'yellow']\n",
    "    time_names = ['Error','Correct','Missing']\n",
    "    ax = frame.plot('frame', \n",
    "                  ['fusion-error-percent-{}'.format(delta), \n",
    "                   'fusion-correct-percent-{}'.format(delta),\n",
    "                   'fusion-missing-percent-{}'.format(delta)],\n",
    "                  'area',\n",
    "                  stacked=True,\n",
    "                  color=time_colors,\n",
    "                  title=\"Fusion stats\"\n",
    "                 )\n",
    "    ax.legend(time_names)\n",
    "\n",
    "    ax2 = frame.plot('frame',\n",
    "                        ['input-error-percent-{}'.format(delta),\n",
    "                         'input-correct-percent-{}'.format(delta),\n",
    "                         'input-missing-percent-{}'.format(delta)],\n",
    "                  'area', stacked=True,\n",
    "                  color=time_colors,\n",
    "                  title=\"Input stats\"\n",
    "                 )\n",
    "    ax2.legend(time_names)\n",
    "    \n",
    "timeplot(frame_elas, 5)\n",
    "timeplot(frame_dn, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO: show arrows pointing to error spikes motivating (e.g. screeshot of car entering frame )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregate Accuracy Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def agggregate_accuracy_comparison(f1, f2, f1_title, f2_title, separate=True):\n",
    "    accs_input = []\n",
    "    accs_input_stds = []\n",
    "    accs_input_e = []\n",
    "    accs_input_stds_e = []\n",
    "    accs_fusion = []\n",
    "    accs_fusion_e = []\n",
    "    accs_fusion_stds = []\n",
    "    accs_fusion_stds_e = []\n",
    "    \n",
    "    MAX_DELTA_MAX = 5\n",
    "    delta_maxes = range(1, MAX_DELTA_MAX)\n",
    "\n",
    "    def stats_for_delta(dframe, name, delta, include_missing):\n",
    "        total = dframe['{}-total-{:.2f}'.format(name, delta)]\n",
    "        error = dframe['{}-error-{:.2f}'.format(name, delta)]\n",
    "        missing = dframe['{}-missing-{:.2f}'.format(name, delta)]\n",
    "        correct = dframe['{}-correct-{:.2f}'.format(name, delta)]\n",
    "\n",
    "        if include_missing:\n",
    "            # Consider missing (uncomputed) values as errors\n",
    "            accuracy = correct / total\n",
    "        else:\n",
    "            accuracy = correct / (total - missing)\n",
    "\n",
    "        return accuracy.mean(), accuracy.std()\n",
    "    \n",
    "    def kitti_acc(dframe, name):\n",
    "        kitti_standard_total = dframe['{}-total-{:.2f}-kitti'.format(name, 3)]\n",
    "        kitti_standard_error = dframe['{}-error-{:.2f}-kitti'.format(name, 3)]\n",
    "        kitti_standard_missing = dframe['{}-missing-{:.2f}-kitti'.format(name, 3)]\n",
    "        kitti_standard_correct = dframe['{}-correct-{:.2f}-kitti'.format(name, 3)]\n",
    "        kitti_standard_score = (kitti_standard_correct) / (kitti_standard_total - kitti_standard_missing)\n",
    "        \n",
    "        return kitti_standard_score.mean()\n",
    "\n",
    "    # We should not count a missing depth value as an error (Peidong)\n",
    "    missing_are_errors = False\n",
    "    for i in delta_maxes:\n",
    "        fus_acc_mean, fus_acc_std = stats_for_delta(f1, 'fusion', i, missing_are_errors)\n",
    "        fus_acc_mean_e, fus_acc_std_e = stats_for_delta(f2, 'fusion', i, missing_are_errors)\n",
    "        accs_fusion.append(fus_acc_mean)\n",
    "        accs_fusion_stds.append(fus_acc_std)\n",
    "        accs_fusion_e.append(fus_acc_mean_e)\n",
    "        accs_fusion_stds_e.append(fus_acc_std_e)\n",
    "\n",
    "        in_acc_mean, in_acc_std = stats_for_delta(f1, 'input', i, missing_are_errors)\n",
    "        in_acc_mean_e, in_acc_std_e = stats_for_delta(f2, 'input', i, missing_are_errors)\n",
    "        accs_input.append(in_acc_mean)\n",
    "        accs_input_stds.append(in_acc_std)\n",
    "        accs_input_e.append(in_acc_mean_e)\n",
    "        accs_input_stds_e.append(in_acc_std_e)\n",
    "        \n",
    "\n",
    "    plot_options = {\n",
    "        'capsize': 4\n",
    "    }\n",
    "    \n",
    "    if separate:\n",
    "        ax = plt.subplot(121)\n",
    "        plt.errorbar(delta_maxes, accs_input, accs_input_stds, label='{}: input'.format(f1_title), **plot_options)\n",
    "        plt.errorbar(delta_maxes, accs_fusion, accs_fusion_stds, label='{}: fused'.format(f1_title), **plot_options)\n",
    "        plt.xlabel(r\"Disparity threshold $\\delta_{\\textrm{max}}$\")\n",
    "        plt.ylabel(r\"Correct pixel ratio\")\n",
    "        plt.ylim([0, 1])\n",
    "        plt.title(\"{}\".format(f1_title))\n",
    "        # plt.axvline(x=3)\n",
    "        ax.spines[\"right\"].set_visible(False)\n",
    "        ax.spines[\"top\"].set_visible(False)\n",
    "        plt.legend()\n",
    "        plt.grid(color='0.7', linestyle='--')\n",
    "\n",
    "        ax = plt.subplot(122)\n",
    "        plt.errorbar(delta_maxes, accs_input_e, accs_input_stds_e, label='{}: input'.format(f2_title), **plot_options)\n",
    "        plt.errorbar(delta_maxes, accs_fusion_e, accs_fusion_stds_e, label='{}: fused'.format(f2_title), **plot_options)\n",
    "        plt.xlabel(r\"Disparity threshold $\\delta_{\\textrm{max}}$\")\n",
    "        plt.ylabel(r\"Correct pixel ratio\")\n",
    "        plt.ylim([0, 1])\n",
    "        plt.title(\"{}\".format(f2_title))\n",
    "        # plt.axvline(x=3)\n",
    "        ax.spines[\"right\"].set_visible(False)\n",
    "        ax.spines[\"top\"].set_visible(False)\n",
    "        plt.legend()\n",
    "        plt.grid(color='0.7', linestyle='--')\n",
    "    else:\n",
    "        # !!! Assume the input is the same in both, and plot input plus the two fused \n",
    "        # results in a single plot.\n",
    "        fig = plt.figure(figsize=(8,6))\n",
    "        ax = fig.add_subplot(111)\n",
    "#         ax.errorbar(delta_maxes, accs_input, accs_input_stds, label='{}: input'.format(f1_title), **plot_options)\n",
    "#         ax.errorbar(delta_maxes, accs_fusion, accs_fusion_stds, label='{}: fused'.format(f1_title), **plot_options)\n",
    "#         ax.errorbar(delta_maxes, accs_fusion_e, accs_fusion_stds_e, label='{}: fused'.format(f2_title), **plot_options)\n",
    "        del plot_options['capsize']\n",
    "        ax.plot(delta_maxes, accs_input, label='{}: input'.format(f1_title), **plot_options)\n",
    "        ax.plot(delta_maxes, accs_fusion, label='{}: fused'.format(f1_title), **plot_options)\n",
    "        ax.plot(delta_maxes, accs_fusion_e, label='{}: fused'.format(f2_title), **plot_options)\n",
    "        ax.set_xlabel(r\"Disparity threshold $\\delta_{\\textrm{max}}$\")\n",
    "        ax.set_ylabel(r\"Correct pixel ratio\")\n",
    "        ax.set_ylim([0.4, 1])\n",
    "        # TODO proper title\n",
    "        plt.title(\"{}\".format(f1_title))\n",
    "        ax.spines[\"right\"].set_visible(False)\n",
    "        ax.spines[\"top\"].set_visible(False)\n",
    "        # TODO spawn legend and grid via ax!!\n",
    "        plt.legend()\n",
    "        plt.grid(color='0.7', linestyle='--')   \n",
    "    \n",
    "        in_score = kitti_acc(f1, \"input\")\n",
    "        first_fusion_score = kitti_acc(f1, \"fusion\")\n",
    "        second_fusion_score = kitti_acc(f2, \"fusion\")\n",
    "        \n",
    "        print(\"KITTI 2015 stereo mean stats:\\nInput:\\t{:.4f}\\n{}:\\t{:.4f}\\n{}:\\t{:.4f}\".format(\n",
    "            in_score,\n",
    "            f1_title,\n",
    "            first_fusion_score,\n",
    "            f2_title,\n",
    "            second_fusion_score\n",
    "        ))\n",
    "\n",
    "\n",
    "#     plt.suptitle(\"Accuracy: {}\".format(\"Missing=error\" if missing_are_errors else \"Missing=ignore\" ))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0m\u001b[01;35mitm-semantics-depth-plots.png\u001b[0m  \u001b[01;35mkitti-depth.png\u001b[0m  \u001b[01;35mlsd-slam-results.png\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "%ls img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Issues with the above plots (OUTDATED NOTES)\n",
    "\n",
    "Right now, we get the unexpected result that the fused data is *less* accurate than the input depth maps. This is so far counterintuitive, since one would expect higher-quality depth maps once several frames are registered. However, this can also lead to an overall loss in precision, since the alignments are never perfect. Our results contradict those from Vineet et al. \"Incremental Dense Semantic Stereo Fusion for Large-Scale Semantic Scene Reconstruction\", who claim that, at least for static scenes, the fusion should, in fact, really help increase accuracy. Sengupta et al.'s evaluation uses the same principle but is not very clear. They only show that their error decreases the higher they set the error tolerance, i.e., that there's very few LARGE mismatches.\n",
    "\n",
    "\"Reconstructing Street-Scenes in Real-Time from a Driving Car\" are very thorough when comparing LIDAR to their sparse reconstructions. For them, single-frame ELAS *does* lead to lower error than maps fused over time.\n",
    "\n",
    "Another interesting issue I've noticed is how the LIDAR-vs-fusion errors are particularly susceptible to bumps in the road. This is most clear in the KITTI odometry sequence 05, where we seem several spikes due to bumps in the road, causing the LIDAR gt to become temporarily misaligned with the map, but NOT with the camera input. Perhaps switching away from GT VO and using e.g. libviso2 VO may help alleviate this issue.\n",
    "\n",
    "TASK(andrei): Update these notes once you try viso2 instead of GT.\n",
    "UPDATE:\n",
    " * Yes, using libviso2 odometry does REDUCE the gap. Looking at the now-vanished spikes in the error of the fused depth maps, it seems that unlike the GT odometry, libviso2 correctly accounts for bumps in the road and reduces the gap in accuracy between the input and the fusion on certain frames.\n",
    " * The fused accuracy is still worse, though, so I need to see whether that can be changed, or whether the result is just sensible.\n",
    "\n",
    "Note that if we count missing pixels as errors, ELAS will always plateau below the fusion since its depth maps are incomplete in the bottom left and right, at least on e.g., KITTI sequence 06, but the fused map obviously no longer has this shortcoming after the first few frames. See:\n",
    "\n",
    "![](img/kitti-depth.png)\n",
    "\n",
    "Remaining issue: figure out WHY the error is higher for the fusion than for the input images (note that for Vineet et al. this *is* the case, at least in the dynamic sequences, unless they activate the semantics-based weighting):\n",
    "\n",
    "![](img/itm-semantics-depth-plots.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "CSV_FILE_DN_W = \"../csv/kitti-odometry-06-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-dynamic-mode-NO-direct-ref-with-fusion-weights-300-depth-result.csv\"\n",
    "CSV_FILE_DN_OLDSCHOOLD = \"../csv/kitti-odometry-06-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-dynamic-mode-NO-direct-ref-NO-fusion-weights-depth-result.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IdqOMGzfOred3xOpdxJy5/mj6cMTnWuDV1dVeuSmBuxAfBuLCjPgwMxwfGw5u4EzdGZde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\nhAhB/x9RNo4ItYEVNwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bc894748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dn_w = pd.read_csv(CSV_FILE_DN_W)\n",
    "# dn_old = pd.read_csv(CSV_FILE_DN_OLDSCHOOLD)\n",
    "# augment(dn_w)\n",
    "# augment(dn_old)\n",
    "\n",
    "# agggregate_accuracy_comparison(dn_w, dn_old, \"Dispnet: dynamic fusion w\", \"Dispnet: fixed w\")\n",
    "\n",
    "# dn_w_300 = pd.read_csv(\"../csv/kitti-odometry-06-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-dynamic-mode-NO-direct-ref-with-fusion-weights-300-depth-result.csv\")\n",
    "# dn_w_600_xsqrt = pd.read_csv(\"../csv/kitti-odometry-06-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-dynamic-mode-NO-direct-ref-with-fusion-weights-2500-x(sqrt(x))-depth-result.csv\")\n",
    "# augment(dn_w_300)\n",
    "# augment(dn_w_600_xsqrt)\n",
    "\n",
    "# agggregate_accuracy_comparison(dn_w_300, dn_w_600_xsqrt, \"Dispnet: dynamic fusion w\", \"Dispnet: more aggressive\")\n",
    "\n",
    "tracking3_dynamic = pd.read_csv(\"../csv/kitti-tracking-sequence-0003-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-dynamic-mode-NO-direct-ref-with-fusion-weights-static-depth-result.csv\")\n",
    "tracking3_no_dynamic = pd.read_csv(\"../csv/kitti-tracking-sequence-0003-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-NO-dynamic-NO-direct-ref-with-fusion-weights-static-depth-result.csv\")\n",
    "augment(tracking3_dynamic)\n",
    "augment(tracking3_no_dynamic)\n",
    "agggregate_accuracy_comparison(tracking3_dynamic, tracking3_no_dynamic, \"Dynamic fusion\", \"No dynamic fusion\", False)\n",
    "\n",
    "fig_fname = \"dyamic-vs-no-dynamic-tracking-03-full-dispnet-separate\"\n",
    "plt.savefig('../fig/{}.eps'.format(fig_fname))\n",
    "plt.savefig('../fig/{}.png'.format(fig_fname))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def compare_agg_acc(file_1, file_2, title, first_label=\"Dynamic fusion\", second_label=\"No dynamic fusion\"):   \n",
    "    first_df = pd.read_csv(file_1)\n",
    "    second_df = pd.read_csv(file_2)\n",
    "    augment(first_df)\n",
    "    augment(second_df)\n",
    "    agggregate_accuracy_comparison(first_df, second_df, first_label, second_label, False)\n",
    "\n",
    "    fig_fname = title\n",
    "    plt.savefig('../fig/{}.eps'.format(fig_fname))\n",
    "    plt.savefig('../fig/{}.png'.format(fig_fname))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KITTI 2015 stereo mean stats:\n",
      "Input:\t0.9527\n",
      "Dynamic fusion:\t0.9717\n",
      "No dynamic fusion:\t0.9670\n"
     ]
    },
    {
     "data": {
      "image/png": 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Tc44ItDpq8999wgLw02mfzsQJ94rcRtYVznZWS5cuJTc31+ULsM4Wd9xCphVe56zOgFfp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\nQggRgmQCF0IIIUKQTOBCCCFECPp/R5ChNXOb3HEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bc07f208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "tracking6_dynamic = pd.read_csv(\"../csv/kitti-tracking-sequence-0006-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-dynamic-mode-NO-direct-ref-with-fusion-weights-static-depth-result.csv\")\n",
    "tracking6_no_dynamic = pd.read_csv(\"../csv/kitti-tracking-sequence-0006-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-NO-dynamic-NO-direct-ref-with-fusion-weights-static-depth-result.csv\")\n",
    "augment(tracking6_dynamic)\n",
    "augment(tracking6_no_dynamic)\n",
    "agggregate_accuracy_comparison(tracking6_dynamic, tracking6_no_dynamic, \"Dynamic fusion\", \"No dynamic fusion\", False)\n",
    "\n",
    "fig_fname = \"dyamic-vs-no-dynamic-tracking-06-full-dispnet-separate\"\n",
    "plt.savefig('../fig/{}.eps'.format(fig_fname))\n",
    "plt.savefig('../fig/{}.png'.format(fig_fname))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KITTI 2015 stereo mean stats:\n",
      "Input:\t0.9033\n",
      "Dynamic fusion:\t0.9121\n",
      "No Dynamic fusion:\t0.9087\n"
     ]
    },
    {
     "data": {
      "image/png": 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CwsKxiWACgUAgcDN9HZaAPNqLtn880G0rJ/lBVCpd0Wno596GISQcvcYPgzyAfqANQ089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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bc07f780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_agg_acc(\n",
    "    \"../csv/kitti-tracking-sequence-0008-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-dynamic-mode-NO-direct-ref-with-fusion-weights-static-depth-result.csv\",\n",
    "    \"../csv/kitti-tracking-sequence-0008-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-NO-dynamic-NO-direct-ref-with-fusion-weights-static-depth-result.csv\",\n",
    "    \"dyn-vs-NO-dyn-tracking-8-full-dnet-sep-20m\",\n",
    "    \"Dynamic fusion\",\n",
    "    \"No Dynamic fusion\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KITTI 2015 stereo mean stats:\n",
      "Input:\t0.8996\n",
      "Dynamic fusion:\t0.9077\n",
      "No Dynamic fusion:\t0.9048\n"
     ]
    },
    {
     "data": {
      "image/png": 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Eh5NFixYZHYJHIT70iA8nHuuio7lfS7rC2YpurID2en3ZyIl0x02iJvcOqsOiMQUGYcJC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\nTxKhxtt4H6sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bc1be0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_agg_acc(\n",
    "    \"../csv/kitti-tracking-sequence-0008-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-25.00-dynamic-mode-NO-direct-ref-with-fusion-weights-static-depth-result.csv\",\n",
    "    \"../csv/kitti-tracking-sequence-0008-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-25.00-NO-dynamic-NO-direct-ref-with-fusion-weights-static-depth-result.csv\",\n",
    "    \"dyn-vs-NO-dyn-tracking-8-full-dnet-sep-25m\",\n",
    "    \"Dynamic fusion\",\n",
    "    \"No Dynamic fusion\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KITTI 2015 stereo mean stats:\n",
      "Input:\t0.8779\n",
      "Dynamic fusion:\t0.8660\n",
      "No Dynamic fusion:\t0.8737\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nThis is terrible, but can be explained by the flakyness of the object detection towards the end, where it\\nconsistently fails to detect the truck right in front of the camera.\\n'"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NwMPDwz1ynLi4OPLy8tzeEe18mF3JrDsfZlcTM+v8EydONOW8vor4MCI+dMSFEbN89PkR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bc1e7668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_agg_acc(\n",
    "    \"../csv/kitti-tracking-sequence-0009-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-25.00-dynamic-mode-NO-direct-ref-with-fusion-weights-static-depth-result.csv\",\n",
    "    \"../csv/kitti-tracking-sequence-0009-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-25.00-NO-dynamic-NO-direct-ref-with-fusion-weights-static-depth-result.csv\",\n",
    "    \"dyn-vs-NO-dyn-tracking-9-full-dnet-sep-25m\",\n",
    "    \"Dynamic fusion\",\n",
    "    \"No Dynamic fusion\"\n",
    ")\n",
    "\"\"\"\n",
    "This is terrible, but can be explained by the flakyness of the object detection towards the end, where it\n",
    "consistently fails to detect the truck right in front of the camera.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KITTI 2015 stereo mean stats:\n",
      "Input:\t0.8839\n",
      "Dynamic fusion:\t0.8720\n",
      "No Dynamic fusion:\t0.8759\n"
     ]
    },
    {
     "data": {
      "image/png": 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ErNfcBPu1cSZm+Qi6BC4IgnA+6lq7+P3WY7z6WQWWLivXXZ7KY9fm0Fl9kGuv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\ngQshhBBhSCZwIYQQIgz9f47u4+un1H8mAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bc2980f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_agg_acc(\n",
    "    \"../csv/kitti-tracking-sequence-0009-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-dynamic-mode-NO-direct-ref-with-fusion-weights-static-depth-result.csv\",\n",
    "    \"../csv/kitti-tracking-sequence-0009-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-NO-dynamic-NO-direct-ref-with-fusion-weights-static-depth-result.csv\",\n",
    "    \"dyn-vs-NO-dyn-tracking-9-full-dnet-sep-20m\",\n",
    "    \"Dynamic fusion\",\n",
    "    \"No Dynamic fusion\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yeah, the difference is quite negligible. May as well use just 20m as a limit.. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KITTI 2015 stereo mean stats:\n",
      "Input:\t0.8562\n",
      "Dynamic fusion:\t0.8730\n",
      "No Dynamic fusion:\t0.8466\n"
     ]
    },
    {
     "data": {
      "image/png": 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wQRCEi9JYBbtfhj1/hI56bbp78Q/Z2zKKmfkrIGKUaaa1dPTwWV2zswrXvtpm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\nZA4/XfJT0mPSB2y742gjz35YwSdHzhI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/51elv2Jh4kJ+ed0vGRl6fkpVVVX56XsHeX1H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\nUsCFEEKIKuj/AwvjEHtiMlRvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bc8e1908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sequence 00\n",
    "compare_agg_acc(\n",
    "    \"../csv/kitti-tracking-sequence-0000-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-dynamic-mode-NO-direct-ref-with-fusion-weights-static-depth-result.csv\",\n",
    "    \"../csv/kitti-tracking-sequence-0000-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-NO-dynamic-NO-direct-ref-with-fusion-weights-static-depth-result.csv\",\n",
    "    \"dyn-vs-NO-dyn-tracking-00-full-dnet-sep-20m\",\n",
    "    \"Dynamic fusion\",\n",
    "    \"No Dynamic fusion\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KITTI 2015 stereo mean stats:\n",
      "Input:\t0.9189\n",
      "Dynamic fusion:\t0.9125\n",
      "No Dynamic fusion:\t0.9272\n"
     ]
    },
    {
     "data": {
      "image/png": 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7lUCk3bHt33cp9fX1PR/kRUg9bEgttAyUHq2mVr688CXbCraxo3gHjW2NjAge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\n1alTx+0hBA1poZMeOulhkRY6t3p4ag2cs5NYW+j8njfA01/8zJrth3l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FkBCiEjKBC+Gs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\nCCFCkEzgQgghRAj6f1yMlQClzXJlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bc298d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sequence 01\n",
    "compare_agg_acc(\n",
    "    \"../csv/kitti-tracking-sequence-0001-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-dynamic-mode-NO-direct-ref-with-fusion-weights-static-depth-result.csv\",\n",
    "    \"../csv/kitti-tracking-sequence-0001-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-NO-dynamic-NO-direct-ref-with-fusion-weights-static-depth-result.csv\",\n",
    "    \"dyn-vs-NO-dyn-tracking-01-full-dnet-sep-20m\",\n",
    "    \"Dynamic fusion\",\n",
    "    \"No Dynamic fusion\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KITTI 2015 stereo mean stats:\n",
      "Input:\t0.9568\n",
      "Dynamic fusion:\t0.9630\n",
      "No Dynamic fusion:\t0.9637\n"
     ]
    },
    {
     "data": {
      "image/png": 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kq/YTTj2sYCA+9BjhQynFWdtZT2+5uYrqxgqqzx6jxnKCmibvKmx7A9WuZuqUA3cn50hw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\ncQInIiKKQv8fXheBydnc69oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bcaa70b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seq 02\n",
    "compare_agg_acc(\n",
    "    \"../csv/kitti-tracking-sequence-0002-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-dynamic-mode-NO-direct-ref-with-fusion-weights-static-depth-result.csv\",\n",
    "    \"../csv/kitti-tracking-sequence-0002-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-NO-dynamic-NO-direct-ref-with-fusion-weights-static-depth-result.csv\",\n",
    "    \"dyn-vs-NO-dyn-tracking-02-full-dnet-sep-20m\",\n",
    "    \"Dynamic fusion\",\n",
    "    \"No Dynamic fusion\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KITTI 2015 stereo mean stats:\n",
      "Input:\t0.8473\n",
      "Dynamic fusion:\t0.8595\n",
      "No Dynamic fusion:\t0.8525\n"
     ]
    },
    {
     "data": {
      "image/png": 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PdfwugI8dO9bqJvgU4sNAXJjxex9Xq+Doa5D3ClR+ylSAgBEQNwkSZ0LGanO1\nrOCwXk/X2NZoykTWseK7uK6YVoexUC02NJaUyBRumXiLUXc6KpVxYePQCKC4upETZXUcO1PH7y7W\ncaJsD3VN+iK0wACN9NHhLJ+aQMa4SGdClEjCQjz7X73f940uWOXD7xK5NDU1MXLkSI+db6gjPgzE\nhRm/9OFwQNFHcOhlOPOevgJ8/ALIvJfmxExCx0yDwJ6DoVKKiqaKa0fT9YVcvnq587gALYAJERM6\na013fKVEphAdqi9Ua7c7KKi8yokyfUR94mIdpy7WY2vR044GBwYwbWwEM8fpi8syxkUxdUwEoSO8\nnxDFL/tGL3jBhyRy6Y6DBw9aUnjdVxEfBuLCjF/5aLgMh38Dh1/Vb42HRkPWA5B5LyTOAODArl0s\nS9L/y2yzt1HSUNJtoL7qkpZ0VNAoUqNSyUrMIjUqlbSoNFKjUpkQMYHgQCOnd2u7g7PlDfzleB0n\nyko5cbGO05fqaW7T57lDRwQwY2wkqzOTOvdYTx4dbllCFL/qG25glQ+/C+CCIAiAniTl3A59tH32\nz/p8dsoyuPnfYPoXO1eC21ptfHjhQz6s+pA3/vYGRXVFXGi4gN1l/jtxVCKpUancPul2Y0Qdmcro\nUaOvWcHd3GbnZFk9Jy5e4mRZHScu1vHp5Qba7Prd0IiQIGaMi+Sri5I7R9ZpCeEEyh5roQt+F8DD\nwnqfp/I3xIeBuDAzbH3UlkDeq/qIu+EihCXA4m9B5n36/Db6rfCjV47wdv7b/LnozzS1NxGkBZGi\npZAek85nUz5ruu0dNqJ7V7aWdk5drNdvg1+s42RZPecqbNgderCOGTWCjKQoHlia1hmsJ8aO8vmE\nKMO2b/QTq3z43Ry4IAh+SHsrnP1AH22f/5v+vckr9KA9dVVnutHa5lreK3iPt/Lf4lztOUYGjeTz\nqZ/nrvS7yIjLIDCg5/nlusY2fSX4RWPOurDyamdClNERIWQ4s5bNTNJvg4+LCr1mhC4IyBx49+zf\nv59FixZZ3QyfQXwYiAszw8JH5TnIe1lfTX61AiKT4KbHYN7X9PKYgEM5OHBpP2/lv8WO4h20OdqY\nFT+LJ294klWpqzpH164+Km0tnCir46TL6PpCtVGFKyl6JBlJkdw1N6lzj/XoyOvXzB4qDIu+4UGs\n8uF3Aby1tfX6B/kR4sNAXJgZsj7amuH0H/TRdvFu0AL1UXbmffqo2zmKrmis4N3z7/J2/ttcaLhA\nRHAEa6eQHv6BAAAgAElEQVSsZXX6aqbGTu08Xbvdwa5zlfzhRD3PnznAibJ6Ltc3d76eEjeK2eOj\nuWdhcmeq0diw4GuaNZwYsn3DS1jlw+8CuCAIw5Tyk3rQPrYNmmshJhVWPAlz74EIPUuW3WFnT+lH\nvHX2Lf5R+g/syk5WYhaPzH2E7InZhAYZo2RbSzvbDlzg13sKKa1pQgMmj27khklxzHTusZ4xLpLI\nUEmIIliD382Bt7e3ExQkf7d0ID4MxIWZIeGjxQYn39YDd9lBCAzWV5Bn3qevKHemKr1ou8jb+W/z\n+3O/p7yxnNjQWO6YfAerJ68mJSrFdMqLtU28tLeI1/aX0NDSzsKUWP5paSqL06KJHDV8boMPhCHR\nNwYRL/iQOfDuyM/PZ/r06VY3w2cQHwbiwozP+lAKLubpQfvEW9Bq03OM3/p/YfaXISwO0Pdqf1i0\ng7fy32LfxX0ALE5azOMLH+em8Tcxokud7OOldWzdXcD7xy6hgFUZY3hwWRpzJ+iJVU6fPk2kL/qw\nAJ/tGxZhlQ+/C+CVlZVWN8GnEB8G4sKMz/loqoXj2/XAXX5cr+aVsVofbU9Y2FkPu7CukLfz3+YP\n5/9AdXM1Y8LG8M053+SuyXcxNtyc8tLhUOw8c4WtuwrYX1hNeEgQ9y9O4f4lKYyPGWU61ud8WIi4\nMGOVD78L4IIgDCGUgpJ9etA+9Xtob4Yxs+G2/4ZZayE0CoDm9mZyi3N58+yb5F3JI0gL4qYJN7Em\nfQ2Lxy2+ZvtXU6udN/NKeXF3IYWVV0mKHsm/3TaddQsmECFz2sIQwe8C+IwZM6xugk8hPgzEhRlL\nfVytdCkkchaCI/TFaJn3wbi5nYd9Wv0pb559k/cL3qehrYGJERP5buZ3uWPyHcSPvLYK2JWGZl7d\nV8xvPi6mprGNOeOj+PlX5rEqYwxBgb2nJZX+YSAuzFjlw+8CeEtLi9VN8CnEh4G4MDPoPhwOKPy7\nHrRPvweONhi/EO74Bcy8q7Pal63VxgdFH/DW2bc4WXWS4IBgspOzuXvK3WQlZnWbGOXM5Xpe2FXI\nu0cu0uZwsHJ6Ig/dmEZWcozbiVSkfxiICzNW+fC7AH7+/HnGjRtndTN8BvFhIC7MDJqP+ktw5Dd6\netPaYhgZAwsf0guJjNYXBnWX2nRy9GQeX/g4X0j7AlEhUdecVinFR/mVbN1VwK78SkaOCOTLCyfw\njSWppMb3PfWl9A8DcWHGKh9+F8AFQfAB7O1wLlcfbZ/9i1FIZMW/w7QvdBYS6S616arUVaxJX8Os\n+Fndjp5b2u28e+QiL+wq5NPyBhIiQvjhrVP56qKJRI8a3glWBP/C7wL4+PHjrW6CTyE+DMSFGa/4\nqCnWS3Ye/q2zkMhoWPIdmPf1zkIirqlNdxbvpNXR2m1q02tOfbWV33xczMv7iqm0tTBtTAQ/XTuH\nL84ZS0jQwGtkS/8wEBdmrPLhdwE8ISHB6ib4FOLDQFyY8ZiP9lb49E96TvLzH+rfm5wNn98MUz7X\nWUiku9Sma6asYU36GlNq064UVNh4YXchb+WV0tzmYPnUBB5cmsaSyXEeLRQi/cNAXJixyoffBfDD\nhw9LIXoXxIeBuDAzYB+V+XrQPvIaNFZC5HhY/rheSCRKH7H0lNr04TkPszJ5pSm1qStKKfYXVrN1\nVwE7z1xhRGAAd81N4oFlqUxJjOh/m3tB+oeBuDBjlQ+/C+CCIHiRtiY49Qc9cBfvgYAgfZQ9/36Y\ndEtnIZHuUpveO/PeblObmk5vd/Cn45fYuquQ42V1xIYF8+1b0vn6Z5JJiAgZnJ9REHwEvwvgERHe\n+et8qCI+DMSFmT75uHxCD9rHtkFzHcSmQfaPYc49EJEIdKQ23XlNatONCzeyfPzya1KbulLX1Mbr\nn5Tw0t4iLtU1k5YQxv+9axarM5MIHTHw+W13kP5hIC7MWOXD74qZCILgIVoa9Fzkea9A2SFnIZHb\nYf59kLy0s5BId6lN75p8F3dOvpNx4b1vvblQ3ciLewp548AFrrbauSEtjoduTGX5lNEEBHhuflsQ\nfAwpZtIde/fuZfHixVY3w2cQHwbiwky3PpSCsjzIewlOvK0XEkmYDp97Gmavg1GxgDO1aaH7qU27\nkldSwwu7CvngxCUCNI0vzhnHA0tTyUi6dr/3YCH9w0BcmLHKh98FcLvdbnUTfArxYSAuzJh8NNXA\nse36bfLyEzBiFMxcrY+2xy/oLCTS19Smpus5FH89eZmtuws5VFxDZGgQ62+cxP2LUxgTZX0ZT+kf\nBuLCjFU+vBLANU27G6gFMpVSm3t5PU0ptcUbbRAEYYAoBUV79KB96l29kMjYufCFZyDjbgiNBPqX\n2tSVqy3tvHHwAi/uKeRCdRMTY0fx4y/OYG3WBMJC/G6MIQhu4/E5cE3TMtED85uapq0HDiql8rq8\njlIqT9O0bKDa9fWueHoO3OFwEBDQe9ECf0J8GIgLJ1cr4cjvUHmvoFXlQ0ikXvlr/n0wdg7gTG1a\ncfSa1KZ3T7m7x9SmXblU18TLe4v53f5i6pvbmZ8cw0PLUlk5YwyBPji/Lf3DQFyY8YIPy+bA1wG5\nzscFQDbQNUBvAlaiB/odXmhDj5w+fZqZM2cO5iV9GvFh4NcuHA4o+FAfbZ/5EzjaaIybTdgdv4SZ\nd3YWEulPatOunCir44Xdhfzx6EUcSrEqYywPLEslc2KMt3/KAeHX/aML4sKMVT68EcCjgWqX53Gu\nLzpH3gWaptUAD3nh+r1SXV19/YP8CPFh4Jcu6i/qaU0PvwK1JTAyFhauh8x7yfu0gmXzlnWb2jQj\nLuO6qU1dcTgUfz97hec/KmRfQRVhwYHce0MK31iSwoTYUYPwgw4cv+wfPSAuzFjlY9AnmDRNi0af\n/34KeF7TtDylVEGXY9YD6wGSkpLYtWsXACkpKYSHh3PixAkAYmNjmT59Onv27AEgMDCQxYsXc+TI\nERoaGgCYN28eFRUVlJaWAtDa2kpVVRWnTp0CID4+nvT0dPbt0/elBgcHs2jRIvLy8rh69SoAWVlZ\nlJWVcenSJQDS09MJDAzkzJkzACQmJpKcnMwnn3wCQGhoKAsWLODAgQM0NzcDsHDhQoqLiykvLwdg\n2rRp2O128vPzARg7dixJSUl0TBeEhYWRmZnJ/v37aW1tBeCGG24gPz+fyspKQK9B29LSwvnz5wE9\nH29CQgKHDx8G9L2Jc+fOZe/evZ2LLJYsWcLp06c7O1x7ezsXLlygqKgIgIkTJxITE8PRo0cBiI6O\nZtasWezevRulFJqmsXTpUo4fP05tbS0Ac+bMoaamhpKSEo99TpMmTSIkJGRQPyebzUZLS4tPfk4Z\nGRnYbDbPfE5FhcRWHSSt5iNCS3ehKQc1MXOwLXqApBUb2LP/IHxawcW6i2w9vpXXTrzGldYrjAwY\nye2pt7MsahkjakdAOdSF19Ec0tzj50TgCC4EjuMXO05R1mAnNlTjB9mTWDpWo6H6CkUnqxgxRH6f\nGhsbAQbvc/Lh3yebzcbZs2d98nMa9N+nkhJsNhsXLlzw2OcUF2ca9/aIN+bANwG5SqkdzsVqaa4L\n2TRNewzYopSq7e71rnh6DrympoaYGN++VTeYiA+DYe+ipkgv2Xnkt9BwCcITYe5XIfPreuIVnKlN\nL+4xpTadnzifNelrek1t2pVKWwuv7CvmNx8XU321lYykSB5alsbnZ41lRODQnDsd9v2jD4gLM17w\nYdkc+DYgy/k4DdgB+shbKVXreqDLQrdBw2azScdzQXwYDEsX7S1w5n092UrBh6AFwOSVcNt/Q/pn\nOwuJdJfa9M4Jd3J/5v29pjbtSn55A1t3FfLOkTLa7A5WTEvkwWWpLEqN9WhhESsYlv2jn4gLM1b5\n8HgAd85xZzlXmNe6rDDfCcxXSm3WNO0xTdMKgNjB3kZWVFTEhAkTBvOSPo34MBhWLirO6gvSjr4G\njVUQNQGW/wvM+2pnIRE9telfeTv/bfZe3AuYU5t+vPdjt4K3Uoo956rYuruAv39aQeiIANbOH88D\nS1NJSwj35k85qAyr/jFAxIUZq3x4ZQ68u6CslJrv8rjHW+aCIPST1kZ9v3bey1CyTy8kMvXzkHkf\nTLq5s5BI19SmiaMS+eacb7qV2tR0uXYHfzh6ka27CjhzuYH48BC+v3IKX/1MMrFhwd76KQVBcOJ3\nWRImTpxodRN8CvFhMGRdXD4Oh16GY29ASx3EToLs/4C590D4aKAjtemfrkltujp9NUvGLek2tWlP\nPmobW/nt/hJe3lvElYYWpiZGsPnu2dwxdxwhQYNTWMQKhmz/8ALiwoxVPvwugMu8jRnxYTCkXLQ0\nwPE39dH2xcMQGAIzbtdH2ylLPZLatKuPosqrvLinkO0HS2lqs7MsPZ6frp3DsvT4IT+/7Q5Dqn94\nGXFhxioffhfAjx49KoXoXRAfBj7vQim96tehl/RCIm1XYfQM+NwmmP2lzkIiPaU2XZO+hqwxWQRo\n7q0CP3r0KEuXLuVAUQ1bdxWQe7qcEQEB3DF3HA8uS2PqGP8qKenz/WMQERdmrPLhdwFcEIYcjdX6\n7fG8V+DKSb2QSMZqyLwfxmeBpqGU4ljFUd46+5YptenjCx93O7WpK+12B/sutvHTX+zhaGkd0aNG\n8K2bJ/P1G5IZHWF9YRFBEPwwgEdHR1vdBJ9CfBj4lAuloHiPPrd96l2wt8C4efCFZyFjTWchEU+k\nNnWlvrmNNw5c4Nd7iiirbSI1PoD/784M7s4cz8jg4Tu/7Q4+1T8sRlyYscqHxxO5eBpPJ3IRBJ/G\nVgFHf6ePtqvOQUgUzF6rz22PnQ2gpza9fOCa1KZrpqxxO7VpV0prGnlpTxGvH7iAraWdRamxPLgs\njRXTRhPgg4VFBGGYY1kiF59m9+7dLF261Opm+Aziw8AyFw4HFPxNH21/+idwtMPEG2DZD2DGHRCs\n5wqvaKzg3fPv8nb+21xouEBEcARrpqxhTfoapsZO7delj16o5fldBXxw4jIAX5g9lgeWpjJ7fDS7\nd+8mICDRYz/mUEd+VwzEhRmrfPhdAPf1Ow6DjfgwGHQXdWV6WtO8V6HOWUhk0Tch815I0AOy3WFn\nT+lH16Q2fXjOw31KbeqK3aHYcbqcrbsKOFBUQ0RIEA8sTeX+xSmMix7ZeZz0DTPiw0BcmLHKh98F\ncH/Y7tIXxIfBoLiwt0P+X/TR9rlcUA5IWw4r/wOm3QZBIUD3qU3vnXEvq9NX9ym1qSuNre28eaiU\nF3cXUlTVSFL0SH70hRmsWzCB8JBr/yuQvmFGfBiICzNW+ZA5cEEYDKoL9XntI78D22UIH6OnNZ33\ndYhNBZypTS98eE1q0zXpa1g+fjkjnHnL+0p5fTMv7y3it/tLqGtqY+6EaB5alsatMxMJGqKFRQRh\nmCNz4N1x/PhxZs2aZXUzfAbxYeBxF+0tcOY9fbRd+A+9kEj6Z/UFaemfhUD916+71KYb5mzgrsl3\n9Sm1aVdOXaxn6+4C/nj0Iu0Oxa0zxvDQjanMT4516/3SN8yIDwNxYcYqH34XwDtquQo64sPAYy4q\nPtWD9tHXoKkaoibCzf+ql+6MSgKcqU3Pf9CZ2jRQC2T5hOW9pjZ1B6UUfz9bwQu7Ctl9rpJRwYF8\ndVEy31iSQnJc31anS98wIz4MxIUZq3y4HcA1TXsIyAYOKKV+6r0mCcIQpLURTv1eD9wXPtYLiUy7\nTR9tp90MAfqt6q6pTSdETODRzEe5c/Kd101t2hvNbXZ+f7iMF3YXkn/FRmJkCBs/N417Fk4kalT/\nbr0LguDbuDUHrmnaNvQ634fRg3i2Umqdl9sGeH4OvL6+nsjISI+db6gjPgz65eLSUX1u+9h2vZBI\n3GR9FfmceyA8AfBcatPuqLK18OrHxby6r5iqq63MGBvJQzemctuscQQHDWx+W/qGGfFhIC7MeMGH\nR+fAY5RSbzsfP69p2ob+tcl6ampqpOO5ID4M3HbRXA8n3tRH25eO6IVEZt6pB+7kJddNbXpb6m1E\nhw4sc9O5KzZe2F3I23mltLQ7uGXaaB5cmsoNk+I8tiJW+oYZ8WEgLsxY5cPdAF6radpqYAewEijw\nXpO8S0lJCcnJyVY3w2cQHwa9ulAKSg9C3kvOQiKNMHomrNqsFxIZqVcj6im16er01cyOnz2g4KqU\nYt/5KrbuLuRvZ64QEhTA6szxPLA0hcmjPV9YRPqGGfFhIC7MWOXDrQCulPqSpmk/BNahz4F/ybvN\nEgQfobEajm1zFhI5BSPCYNbd+tx20nzQND216aX916Q2ffKGJ/ud2tSV1nYH7x27yNZdhZy6VE9c\nWDDfy57C1z4zkbjwEA/9oIIgDDV6DOCapv1KKfWw8/HTzm8XAvGapj2llHpiMBroaVJSUqxugk8h\nPgw6XSgFRbv1Wtun/uAsJJIJX/yZXkgkRB/teiO1qSt1jW387pMSXtpbSHl9C5NHh/P06lncOS+J\n0BHeLywifcOM+DAQF2as8tHbCPxNl8fbvN2QwSI8PNzqJvgU4sMgQmuC3c/oo+3qAgiNgvn36XPb\nY/Q9nt5IbdqVkqpGXtxTyBsHL9DYamfp5HieXjObm9ITBrWwiPQNM+LDQFyYscpHjwFcKbXT5el5\npVR9xxNN01K82CavcuLECSlE74L4cHL0dSJ//wgoO0xcDDdt1AuJjNBzg1+0XeSdc+/wTv47ptSm\nd6XfRWpUqkeacKi4muc/KuSvpy4TGKBx+5wkHliayoxx1iwWkr5hRnwYiAszVvlwdxHb8+jz3x3k\nALd6vjmCYAEl++EP36Y+agbRX30REqYAztSmRX81pzYdt5iNCzcOKLWpK+12B385Wc7W3QUcLqkl\nauQIvnnTJO5bnEJi5MBH84IgDF96DeCapq1BD9zZmqb9pePbXm+VF4mNdS+NpL/g9z7qSmHb1yBq\nPJeX/RfRCVO8ltrUFVtLO9sOXODXewoprWkiOW4UP7ljJnfPH8+oYN9IkOj3faML4sNAXJixyoe7\niVyeVko9PgjtuQZPJ3JxOBwEBEgBhw782kdrI7x4K1QX0v7AX3m/IZ+389/2aGrTrlysbeKlvUW8\ntr+EhpZ2FqbE8sCyVLKnJxI4iPPb7uDXfaMbxIeBuDDjBR+eS+RiVfD2Bnv27JG5Gxf81odS8O4j\ncPk46iuv8+P83/Hu+Xc9ltq0K8dL69i6u4D3j11CAasyxvDgsjTmThhYQhdv4rd9owfEh4G4MGOV\nD7cCuKZpDwLfBFKBGkAppdK92TBB8Cq7fgon34Hs/+Cltku8e/5dVkWv4unbnx5QalNXHA7FzjNX\n2LqrgP2F1YSHBHH/4hTuX5LC+JhRHrmGIAj+i7uTbWuVUlkdt9I1TXvOq63yIoGB3t8/O5TwSx9n\n3oe//SfM+hIfTpzNMx9+l1tTbuWOoDs8ErybWu28mVfKi7sLKay8SlL0SP7ttul8acEEIkOHTmER\nv+wbvSA+DMSFGat8uDsH/oZLNrbzwKbBGoF7eg5c8HPKT8ILn4X4KXx658+4N/chUqNS+fXnfs3I\noJEDOvWVhmZe3VfMbz4upqaxjdnjo3hwWRqfzxhDUKDMFwqC4DZuzYG7+7/K/wIopf4LmAQM2VSq\nR44csboJPoVf+bhaBa99BYLDqbzzf/n2Rz8gfEQ4/3PL/zAyaGS/XZy5XM8Ptx9l6dMf8r8fnmNB\nSixvbLiBd//PEm6fM27IBm+/6htuID4MxIUZq3y4ewv9/2iadlgp1eAM4kOWhoYGq5vgU/iND3sb\nbL8PGi7Tet+7fO/g09Q01/DSqpcYPWo00DcXSik+yq9k664CduVXMnJEIF9eOIFvLEklNX5guc99\nBb/pG24iPgzEhRmrfLgbwGuAIk3T3uj4RkeedEEYEvz5cSjahbozh/8oeY8jFUf46U0/ZWbczD6d\npqXdzrtHLvLCrkI+LW8gISKEH946la8umkj0qGAvNV4QBOFa3J0Dn9f1e0qpw15pURc8PQdus9kk\nj68LfuHjwAvw/j/Dkkd5YUwyz+Y9yyNzH+HhOea/QXtzUXO1ld98XMzL+4qptLUwbUwEDy5L44tz\nxhISNDwX9PhF3+gD4sNAXJjxgg+P7gMflGA9GFRUVEjHc2HY+yjaDR88Bumf5W9TbuRnf/9nVqWs\n4puzv3nNod25KKiw8cLuQt7KK6W5zcHyqQk8uDSNJZPjBlTbeygw7PtGHxEfBuLCjFU+hubqmgFQ\nWlpqdRN8imHto6YItn0dYtP49JaNPL77X5gZN5OfLPlJt8G3w4VSio8Lqnjw5QOs+H//YPuhUu6Y\nk8Rfv3cjL31jIUvT44d98IZh3jf6gfgwEBdmrPLhG0mXBcHTtNjgtXtA2alc/Rzf3vMEEcER/OyW\nn/VY8rPdoXj3SBlbdxVyvKyO2LBgvn1LOl//TDIJESGD/AMIgiD0jt8F8EmTJlndBJ9iWPpwOOCd\nDVBxmpZ7Xue7R56hprmGl1e93LnivCsHiqp5fFcz5bYjpCWE8X/vmsXqzCRCRwzP+W13GJZ9YwCI\nDwNxYcYqHz0GcE3TngZ6XOGmlHrCKy3yMiEhMpJyZVj6+MfTcOY91K1P8ePLf+doxVH+3/L/x4y4\nGd0efqG6kfWvHCQ8OJAX75/D8imjCfCxwiJWMCz7xgAQHwbiwoxVPnqbA98GvOHytQ34wOXxkOTU\nqVNWN8GnGHY+Tr4D/9gE877GC2HBvFfwHt+a+y1WJq/s9nBbSzsPvnwQu0PxnbmB3DItUYK3k2HX\nNwaI+DAQF2as8tHjCNx15bmmaauBLwNRSqlbNU3bhl4nXBB8h0tH4Z2HYcIids6+nZ999ANWpa5i\n/ez13R7ucCi+t+0I5ypsvPSNBXD5zCA3WBAEof+4uwp9g1LqS0Ch83mMl9rjdeLjPVcicjgwbHzY\nruiL1kbFcubWH/PE3h8xK34WP1nc/YpzgP/O/ZTcU+X8223TWZaeMHxceAjxYUZ8GIgLM1b5cHcR\nW52zpGiMczRe68U2eZX0dKmC6sqw8NHeqm8Xa6yi8quv8+2PnyQyOJKf3dzzivN3j5Txiw/P8+UF\nE7h/cQowTFx4EPFhRnwYiAszVvlwawTuHH3HoKdUjXU+H5Ls27fP6ib4FEPeh1J6lrULH9Ny+894\n9PTz1LXU8fNbfk7CqIRu33L0Qi2PvXmMhSmx/OSOjM4R+pB34WHEhxnxYSAuzFjlw60RuKZpka5F\nTDRNm6uUknI0gvXsz4HDr6KW/YAn6w5zrOIYzyx/hulx07s9/Ep9M+tfPUh8eAi/+lomwUF+l8tI\nEIRhgrv/e23VNO0WAE3THgI2eK9J3iU4WApOuDKkfZz/G/zlCZh6G1vjE3m/4H2+Pe/bZCdnd3t4\nc5udh149RENzO1vvyyIu3Lz1Y0i78ALiw4z4MBAXZqzy4VYxE+jcF74CeEop9bZXW+WCp4uZCMOE\nqvPw/C0QOY4dn/0XvrfnX/h86ud5etnT3S5aU0rxz28c5Z3DZTz3tfl8LmOMBY0WBEFwC7f2sro1\nAtc0bQWQBjwOfFnTtJR+N8ti8vLyrG6CTzEkfTTXwWtfAS2A05//T/5l///H7PjZPeY4B8j5qIB3\nDpfxzyun9Bi8h6QLLyI+zIgPA3Fhxiof7t5Cz1ZKfUkptdO5gG1jbwdrmna3pmnZmqY91s1rmZqm\nKU3Tzju/cvrT8P5y9erVwbyczzPkfDjs8NZDUH2eyjt/zrcPPEVUSBQ/u+VnhAR2nw1p5+lyNv35\nDLfNHsu3b5nc46mHnAsvIz7MiA8DcWHGKh+9pVL9lVLqYeOp9pTLyz1uI9M0LRNAKbVD07Q0TdMy\nlVKuf57EKqU0l2OH7JY0wQJ2/gTy/0LLqk08eu416lvreWXVK8SP7H4f5tnyBh59/Qgzx0Xy07vn\n+EUVMUEQ/IPeVqG/6fK4I3Wq4vr35tcBuc7HBUA20BnAlVI7XI7NUkptca+pniErK2swL+fzDCkf\nx96APc+i5n+Df28p5FjlMZ5d/izTYqd1e3jN1VYefPkgoSMC2fL1LEYG916YZEi5GATEhxnxYSAu\nzFjlo8db6EqpnS5Pa9AD82ZgPXC+l3NGA9Uuz+O6O0jTtGz0vOqDSllZ2WBf0qcZMj7KDsG734Lk\npTw/YSp/KvwTj2Y+yorkFd0e3mZ38H9+l8fluma23DufcdEjr3+JoeJikBAfZsSHgbgwY5UPdzOx\n5aLPez8FLAB2Ov8dCCu7jMY70TRtPfofCiQlJbFr1y4AUlJSCA8P58SJEwDExsYyffp09uzZA0Bg\nYCCLFy/myJEjNDQ0ADBv3jwqKio6C663trYSExPTmXw+Pj6e9PT0zo34wcHBLFq0iLy8vM55jays\nLMrKyrh06RKgZ90JDAzkzBk9d3ZiYiLJycl88sknAISGhrJgwQIOHDhAc3MzAAsXLqS4uJjy8nIA\npk2bht1uJz8/H4CxY8eSlJREx4r7sLAwMjMz2b9/P62trQDccMMN5OfnU1lZCcCMGTNoaWnh/Hn9\n76nx48eTkJDA4cN6GvuIiAjmzp3L3r17sdvtACxZsoTTp09TXa3/jdXe3k5ISAhFRUUATJw4kZiY\nGI4ePQpAdHQ0s2bNYvfu3Sil0DSNpUuXcvz4cWpr9dmPOXPmUFNTQ0lJicc+p0mTJhESEsKpU6cI\nbqki89APCApP5Jfxy3ju6C/5TORneCDjgR4/p5dPNLG3uI0nP5fK1eIT7Cq+/udks9mYMGGCT35O\nGRkZ2Gy2Qf2cbDYbkydPdvtzguH9+9TY2MjkyZN97nPq6++TJz4nm82Gw+Hwyc/Jit8nm81GSEiI\nxz6nuLhux73XopS67hfwXJfnb/Ry7Cb0RW8AdwOP9XBcrjvXnj9/vvIkH330kUfPN9TxeR+tTUpt\nuVmp/xyrTn76B7XgNwvUPe/fo5rbm3t8y28+LlLJG99T//neyT5dyuddDDLiw4z4MBAXZrzgw63Y\n7A6JeuMAACAASURBVO4IPMtZgawaiAXSNE37lfMPgIe7HLsN6JgQSAN2AGiaFq2UqnU+TnPzuh5H\ncvia8WkfSsEfH4WyQ1Tc9Su+ffRZfcX5zT2vON93voon3z3J8qkJPL6q+2xsPeHTLixAfJgRHwbi\nwoxVPtwN4A+5e0KlVJ6maVnOOe5aZaxA3wnMdzm0wN1zepLAwN4XMvkbPu1j78/h2Os03/Q4j5a+\nR0NrA6+uerXHFeclVY088ttDJMeN4n++Mo/APtb19mkXFiA+zIgPA3FhxiofbgVw5VIb3M3jr1lZ\nrpSa7/K4AIvSsZ45c4aEhO6LXPgjPuvj7F8h999R0+/g3wOqOV55nGdvfpapsVO7PdzW0s5DrxzE\noWDrfQuIDB3R50v6rAuLEB9mxIeBuDBjlQ+p5CD4HhVn4a0HYMwstqQv4IOiP+srzid2v+Lc4VB8\n9/UjnKuw8Yt7MkmNDxvkBguCIAw+fhfAExMTrW6CT+FzPppq4LUvQ1AIf73xEf73+Ba+mPZFHsh4\noMe3/Hfup+w4Xc6PbpvO0vTub6+7g8+5sBjxYUZ8GIgLM1b5cHcOfNiQnJxsdRN8Cp/yYW+H7d+A\n2hJOrvkF/5r3U+YkzOHJxU/2mEHt3SNl/OLD83x5wQTuW5wyoMv7lAsfQHyYER8G4sKMVT7cLWYS\n2eV5ijcaMxh07FkUdHzKR+6/Q8GHXLn1J3zn1BZiQmN49uZne1xxfvRCLY+9eYyFKbH85I6MAadJ\n9SkXPoD4MCM+DMSFGat8uHsL/fkuzwe1AIngBxz+DXz8C5oXrufRil00tDbw81t+3uOK8/L6Zta/\nepD48BB+9bVMgoP8bjZIEAQ/p9db6JqmrUFPoZqtadpfOr7t9VZ5kdDQUKub4FP4hI+S/fDe91Cp\nN/GjUQ5OFp/kZzf/rMcV581tdta/eoiG5nbeengxceHdj9D7ik+48CHEhxnxYSAuzFjlQ1N6VrTe\nD9K0p5VSjw9Ce64hKytLdaTZE4YhdaWw5WYICedXi7/OL0/+mu9mfpcHZnW/aE0pxT+/cZR3Dpfx\n3Nfm91jbWxAEYQjj1kDZrfuOVgVvb3DgwAGrm+BTWOqjtRFe+wq0NfGX5d/hlyd/ze2TbuefMv6p\nx7fkfFTAO4fL+P7KKR4P3tI3zIgPM+LDQFyYscqHu4vY/tLl+baejvV1OpLsCzqW+VAK3n0ELh/n\n5Od+zL8d+yXzRs/jyRt6XnG+83Q5m/58hi/MHsu3bpns8SZJ3zAjPsyIDwNxYcYqH+6u/On6P2qM\npxsi+Bm7fgon36F8+Q/5zrnXiA2N5ZnlzxAcGNzt4WfLG3j09SPMHBfJf909Z8ArzgVBEIY67s6B\nPwecQy9MshJYoJT6kpfbBnh+DrylpYWQEM8sehoOWOLjzPvw+j00ZazhG6GNFNYV8sqqV3pctFZz\ntZU7frGHxlY7f/jWErdqe/cH6RtmxIcZ8WEgLsx4wYdH58C/CdQBjwI1gxW8vUFxcbHVTfApBt1H\n+Ul4ez1q3Dx+FBfFqapTbLpxU4/Bu83u4JHf5nG5rpkt9873WvAG6RtdER9mxIeBuDBjlQ9358BX\no4+8xymltg7lOfCOwvKCzqD6uFqlL1oLDue5OZ/nLyU7+N7877F8wvIe3/KTP55iX0EVT62eReZE\n787cSN8wIz7MiA8DcWHGKh/uzoFvcI66O0qAyhy40DfsbbD9Pmi4zJ9vfpRfnnmV2yfdzv0z7+/x\nLb/5uJhXPy5m/Y1prJk/fvDaKgiCMARwN4DXaZr2IBDrHI3XerFNXmXatGlWN8GnGDQff34cinZx\nIvtx/u30i9ddcb7vfBU//sNJlk9NYOPnBqeN0jfMiA8z4sNAXJixyoe7AXwj+qi7BogdynPgdrvd\n6ib4FIPi48ALcGAr5Yse4jsX3iN+ZDzP3vxsjyvOS6oaeeS3h0iOG8X/fGUegQGDs+Jc+oYZ8WFG\nfBiICzNW+XA3gD8N5CilvqmU2urNBnmb/Px8q5vgU3jdR9Fu+OAxmiat4DvtF7jadpWf3/JzYkNj\nuz3c1tLOQ68cxKFg630LiAwd4d32uSB9w4z4MCM+DMSFGat8uFtOtBYo1DTtjY5vKKUe9k6ThGFD\nTRFs+zqOmBT+dVwSp0v/wc9v+TnpMendHu5wKL77+hHOVdh4+RsLSY0PG9z2CoIgDCHcDeDvAc95\nsyGDxdixY61ugk/hNR8tNnjtHlB2fjX/LnLzt/H9+d/npgk39fiWn/71U3acLufHX5zB0vTuq5B5\nE+kbZsSHGfFhIC7MWOXD3QD+NeBBpVSDNxszGCQlJVndBJ/CKz4cDnhnA1Sc5oNb/5XnPn2ZOyff\nyX0z7+vxLe8eKeOXfz/PVxZO4L7FKZ5vkxtI3zAjPsyIDwNxYcYqH+7OgdcARZqm/UrTtOc0TfuV\nNxvlTaSymRmv+PjH03DmPY7f+Cg/Ovc6maMz+dFnftTjivOjF2p57M1jLEyN5T9uz7AsTar0DTPi\nw4z4MBAXZqzy4e4IPMf5JQi9c/Id+McmLs9Zy6MVHxE/Mp5nbu45x3l5fTPrXz1IfHgIv/pqJsFB\n7v5NKQiC4N+4m0r1MJAFPA6scD4fkoSFycIoVzzq49JReOdhGics4DtBdTS2N/a64ry5zc76Vw/R\n0NzO1vuyiAu3Nrey9A0z4sOM+DAQF2as8uFuMZNtwDbgMJANZCul1nm5bYDni5kIXsJ2BbbcjAPF\nD2YvZ0fZbv53xf9y4/gbuz1cKcX3th3h90cu8tzX5nu8trcgCMIQxnPFTIAYpdTbSqlCpdTzwKT+\nt8ta9u/fb3UTfAqP+GhvhW1fh8Yqfpm1htyyXXw/6/s9Bm+A5/5RwO+PXOT7K6f4TPCWvmFGfJgR\nHwbiwoxVPtwN4LWapq3WNC1S07Q1GDnRhxytra1WN8GnGLAPpeD9f4YLH/OnGx8mp/D33DX5Lu6d\ncW+Pb9lxqpzNfznDF2aP5Vu3TB7Y9T2I9A0z4sOM+DAQF2as8uHuHPiX0EfdW4HUoZxKVfAw+3Pg\n8KscW/RP/Kj4D8xPnN/rivOz5Q08+vphMsZF8V93z7FsxbkgCMJQp8c5cE3TfgBsUUrVd/l+FPC4\nUuqJQWifx+fA29vbCQpyd/H98GdAPs7/DX6zhsvpK/hKUBUhgSG8dttrxIR2X6yu5mord/xiD42t\ndv747f+/vTuPj6rK8z7+uYQsGgIhJIaQECBh30kCyKKIgNOP4zItKCKLjzaGaRrHHdruaVud7kcB\np7ttUASxn1agAcFWYHw9rWCPDjuBEEA2MWnCHgghQCJZ6z5/5BY3J1RVAlTVrar7e79evJJKnao6\n+dYNv7ucOmcYSa18t7b3jZBtQyV5qCQPk2Sh8kEeN30NfGDD4g2g6/pFIONGe2U1mcNXdcN5nM+H\nVU/wQ0I3/q2FzpWaK8y/e77b4l1d62D6slzOXKxg0ZTMgCveINtGQ5KHSvIwSRYqq/LwVMA9rfkd\ntOc9i4uLre5CQLmhPCouwvIJOLRm/LJzXw6Xfs/cO+fSubX769mvrzvA1oLzvPFQHzJSA3M5edk2\nVJKHSvIwSRYqq/LwVMBzjTXAFcap9VzfdUkENEctfPIUlOQzf+A4NpzeyguZL3BHyh1uH7J0WyFL\nthWSfWcaYzNT/NhZIYQIXW4LuK7rPweyNE07Um8K1e+BdOO+oNSzZ0+ruxBQrjuPr16HI1/w+dAn\neP/43xjbZSyTe05223xr/nleXbufkd0SmPUjaxa9byrZNlSSh0ryMEkWKqvy8HjVXdf1fzUGrWUZ\nP5plXAMPWpWVlVZ3IaBcVx57P4bNf2Bvv7G8cvq/yUrM4peDf+l2JPmx8z/w02W76BgfzdsTBhDW\nLLCvvMi2oZI8VJKHSbJQWZVHox8j03X9oq7rXxn/grp4A+Tn51vdhYDS5DxO7oI1MzjT4Xb+raqA\n2269jd/d9TvCw8JdNi+rrGHqRznoOiyekkXLKNftAolsGyrJQyV5mCQLlVV5yMoRonGXTsOKifwQ\nk8jTcbdSWVvJ/FHuR5w7HDrPrsgj/1w5707MoGO8zJsshBDeZrsCnpIig6jqazSP6gpYORFHxSV+\n0W0g310sYM6dc0iPdT+b7ltfHmbDwSJ+9c89GNY53ss99h3ZNlSSh0ryMEkWKqvysF0BT0hIsLoL\nAcVjHroO656Bk7uYP3AsX53N4aWslzyOOF+Td5J3v85nwqD2PD60o/c77EOybagkD5XkYZIsVFbl\nYbsCvnt30K6E6hMe89gyD/auYN3ACbx/+mvGdhnLxB4T3Tbfc7yUmav3MqhTHK890DvopkmVbUMl\neagkD5NkobIqD9sVcNFE330J618hr9toXi3JYWDbgR5HnBddquCpj3aSEBPJgokZRDSXTUsIIXzJ\ndpPZxsTEWN2FgOIyj3PfwSc/4XRST57RzpEYncjvRrgfcV5RXUv2Rzspq6zhkyeH0qZFpI977Ruy\nbagkD5XkYZIsVFbl4XYxk0Dh7cVMRCOuXID3R/FD1SWmpPfi5JWzLLt3GWmxaS6b67rOcyvz+Czv\nFAsnZ/JPvQJjbW8hhAhiN72YSUjasmWL1V0IKEoetTWw6gkcpcd4uccQjlw+ytwRc90Wb4D3ving\ns7xTvDCma9AXb9k2VJKHSvIwSRYqq/KwXQGvra21ugsBRclj/StQ8N/My/oxfy/OY+bAmQxPHu72\nsRsOFDHni0Pc1zeJGXe7X8gkWMi2oZI8VJKHSbJQWZWH7Qq4cGP3Utj2Duv63s/ic9sY13Ucj3V/\nzG3z74ou88yK3fRu14q54/oF3YhzIYQIdra7Bu5wOGjWTPZbnBwOB81O5MCH95GXmsGTzc7R/7b+\nLByzkPBmrgetXSiv4sF3NnOlupa1M4YF5NreN0K2DZXkoZI8TJKFygd5WHcNXNO0cZqmjdY0baab\n+zOMNuN88fqeHDx40N8vGdCO7PoaVk7iVGwyz0ReISk6qW7EuZviXV3r4KfLdnHmUgULJ2eGTPEG\n2TYakjxUkodJslBZlYfXC7imaRkAuq5vAEqdtxt4Wdf11UCam/t9pqSkxJ8vF9iqfiD5f17kh5or\nPJ2cQrWjhnmj5hEbFev2Ia+vO8C2ghLefKgPGamu50IPVrJtqCQPleRhkixUVuXhiyPw8UCp8X0B\nMLr+ncZRdw6ArutzdF3P9UEfRGN0HdZM55bLBczqNZz8shO8NeIt0lq5H3G+dFshS7YVMu3ONB7K\nkLmQhRDCSr4o4LFA/d2RNg3uHwi0MU6juzzF7ku9e/f290sGpo1vwf5P+V2ff+LrC/t5aeBLDE0e\n6rb51vzzvLp2PyO7JTDzR9392FH/kW1DJXmoJA+TZKGyKg+rZmI7r+t6rnGdfJxxOv0qTdOygWyA\n5ORkNm7cCEDHjh1p0aIF3377LQBxcXH06NGDzZs3AxAWFsbQoUPJy8vj8uXLAAwYMIBz585x4sQJ\nAFq1aoXD4eDAgQMAxMfH06VLF7Zu3QpAREQEgwcPJjc3l/LycgCysrI4efIkp0+fBqBLly6EhYVx\n6NAhABITE+nQoQM7duwAICoqioEDB5KTk0NFRQUAgwYNorCwkKKiIgC6d+9ObW0tR44cASApKYnk\n5GScA/aio6PJyMhg+/btVFVVATBkyBCOHDlCcXExAD179qSysvLqWrQpKSkkJCRcnZc3JiaG/v37\ns2XLlqsfcxg2bBgnNiwkdctvWJqcyUdlB7g3+V7an2vPxuKNpKam0rp1a/bs2QNAbGwsrdql89Sf\nt3PbrRoTOlYS1kxj3759lJbWnWjp168fFy5c4NixY157n9LT04mMjPTr+1RVVcXw4cMD5n06ePDg\n1VNzvXv3pqysjKNHjwK4fJ/69OnDpk2b0HUdTdMYPnz4Tb1PNTU1jBw5MuDeJ7Dm76lZs2YMGzYs\n4N4nK/6eqqqqaN++fUC+T1b8PVVVVdG1a1evvU9t2jQ87nXN66PQNU2bDazXdX2Dcbo8Tdf1OfXu\nnwkU6Lq+2ijU6bquz3L3fN4ehb5x40buuMP9alohr2g/fHAPuxM68ZOoH0iLSGP5w8vdDlq7XFHN\n2AVbKLpUyZqfDQvptb1tv200IHmoJA+TZKHyQR6WjUJfCTgvpKYBGwA0TXOOjFpd7/5YjOvhwg/K\nz8PyCZyMasGzLSApOompiVPdFu9aR900qfnnynl3YkZIF28hhAg2Xi/gzkFpmqaNBkrrDVL7yri/\ngLrR6eOANg1Pn/taamqqP18ucNRWw6rHKS8r4ukOaVTrDuaPmk+PTj3cPuQ/vzzMhoNneeW+ngzr\nHO/HzlrDttuGG5KHSvIwSRYqq/LwyTVwXdcXufhZpov7/Vq8AVq3Dq2PPjXZ335O7dGN/LzfKAou\nF/Du6Hfp1KoTl7RLLpuvyTvJu1/nM2FQKlOGdPBzZ61h223DDclDJXmYJAuVVXnYbiod5yAFW8n5\nAHIW83bvUXx96QizBs1iaLu6Eeeu8thzvJSZq/cyqFMcrz3QyzbTpNpy2/BA8lBJHibJQmVVHrYr\n4LZzdBP8v5l8ljaI/1t+hPHdxjOh+wS3zYsuVfDURztJiIlkwcQMIprLJiKEEIHIqo+RWSY21v0s\nYyHnwlFYOZnc+A68phUzuO1gZg1SB/zXz6Oiupbsj3ZSVlnDJ08OpU2LSD932Fq22jaaQPJQSR4m\nyUJlVR62W8zENirL4IN7OFl+kgkpKbSMas2ye5fRKrKVy+a6rvPsyjzW5J1i4eTMoF/bWwghgph1\ni5kEsk2bNlndBd9zOODTaZQXH2JGx27UoDPv7nkui7czjwXf5LMm7xQv3tPVtsXbFtvGdZA8VJKH\nSbJQWZWH7U6hB/oZB6/45k1qD/0Xs3rfwT9+OMGC0Qvo1KqTy6a6rrPhQBFzvzjM/f3a8bORnf3c\n2cBhi23jOkgeKsnDJFmorMrDdkfgIT+iev+n8M1s/tBtKN+UF/LzQT9nSLshbpufLHPwzIrd9G7X\nijlj+4Z+Ph7Y+Xd3RfJQSR4myUJlVR5yDTyUnN4DH/wTn7brzCthF3m026P88vZfum1+obyKB9/Z\nzJXqWtbOGBZSa3sLIUQQk2vgruzbt8/qLvhG2VlY/hi7WsbxevNybk+6/ZoR5/VV1zr46bJdnC79\ngYWTM6V4E8Lbxg2SPFSSh0myUFmVh+0KuHMlmZBSUwUrJ3Oi8gLPxceSEpPCWyPeonkz90McXlu3\nn20FJTzZJ4qMVJlVCUJ027gJkodK8jBJFiqr8rBdAQ85ug6fP0/Zie08ndaDGg23I86dlmwrZOm2\nY0y7M43hKRF+7KwQQghvsd0o9H79+lndBe/avpDa3UuY2WMw/6g8y3tj3qNjq45um2/JL+bVtfu5\nu/ttzPxRd8rLLvuvrwEu5LaNmyR5qCQPk2ShsioP2x2BX7hwweoueE/+3+GLl/l9egYbK07zi8G/\n4Pak2902P3b+B6Yvy6VTfDRvP9qfsGZaaOVxkyQLleShkjxMkoXKqjxsV8CPHTtmdRe843w+rHqC\nvyal86GjmAndJ/BIt0fcNr9cUc3Uj3LQdVg8JYuYqLo1wEMmDy+QLFSSh0ryMEkWKqvysN0p9JBQ\ncRGWT2BnRHP+I6qWoUlDmTlwptvmtQ6d51bmkX+unI+eHETH+Gg/dlYIIYQv2O4IvGPHjlZ34eY4\nauGTpzh+6SjPJSaQEpPC3BFzPY44f+vLw2w4eJZX7uvJsM7xyn1Bn4cXSRYqyUMleZgkC5VVediu\ngLdo0cLqLtycr16n7PsveTqtB45mYcwfNZ+WES3dNv9s90kWfJ3PhEGpTBnS4Zr7gz4PL5IsVJKH\nSvIwSRYqq/KwXQH/9ttvre7Cjdv7MbWb/8BLXfpTWHWJ3931Ozq0vLYoO+UdL2XmJ3sZ1CmO1x7o\n5XK6v6DOw8skC5XkoZI8TJKFyqo8bFfAg9bJXbBmBv/ZsRebqs/z8uCXGZw02G3zoksVZH+0k9ti\nInlvUiYRzeWtFkKIUGK7QWxxcXFWd+H6XToNKybySZtElmiXmdhjoscR5xXVtWR/tJOyyhr++pOh\nxEW7n6wlKPPwEclCJXmoJA+TZKGyKg/bLWbicDho1iyIjkarK+DP95JT+j3ZiXEMTrqd+aPmux20\npus6z67MY03eKRZOzmx0be+gy8OHJAuV5KGSPEyShcoHechiJq5s3rzZ6i40na7Dumc4XrSH59sl\n0b5lKnNGzPE44nzBN/msyTvFi/d0bbR4Q5Dl4WOShUryUEkeJslCZVUetivgQWXLPC7vW8mMtB7o\nYeHMv9vziPMNB4qY+8Vh7u/Xjp+N7OzHjgohhPA3210DDwsLs7oLTfPdl9Ssf4WX0ntxrKachWMW\nktoy1W3zw2cu88yK3fRu14o5Y/s2eYH5oMnDDyQLleShkjxMkoXKqjxsdw08KJz7DhaPYvZtt7G0\neRWvDHmFh7s+7LZ5SXkVD76ziYpqB+tmDKdtqyg/dlYIIYSXyTVwV/Ly8qzugmdXLsDyR1nd4haW\nNq9iUo9JHot3da2D6ct2UXSpkkWTM6+7eAd8Hn4kWagkD5XkYZIsVFblYbsCfvlyAC+fWVsDq54g\n58oZftvqFoYlD+OFrBc8PuS1dfvZVlDCmw/1YUBq6+t+yYDOw88kC5XkoZI8TJKFyqo8bFfAA9r6\nVzh2bCPPJSeT2rIjc+/0PMf5km2FLN12jGl3pvFQRoofOyqEEMJqtrsGXlZWFpjz+O5eyuW1M5iY\n3p2S5s1Zfu9y2rds77b5lvxiJn+wgxFdE3h/ShZhzZo2aK2hgM3DApKFSvJQSR4myULlgzzkGrgr\n586ds7oL1zq2nZr/eo6XOnbluKOS39/1e4/Fu/B8OdOX5dIpPpq3H+1/w8UbAjQPi0gWKslDJXmY\nJAuVVXnYroCfOHHC6i6oLp6AlZP4z8QkNnOFf7/93xnYdqDb5pcrqpn64U50HRZPySImKvymXj7g\n8rCQZKGSPFSSh0myUFmVh+0+Bx5Qqn6A5RP4OLyWpZE6k3pMYmzXsW6b1zp0nl2RR0FxOUueHETH\n+Gg/dlYIIUQgsd0ReHp6utVdqKPrsGY6O0q/4424GIYnD290xPlbXx7mq0Nn+fX9PRnaOd4r3QiY\nPAKAZKGSPFSSh0myUFmVh+0KeGRkpNVdqLPxLQoPr+O55BQ6tOzEnDs9z3H+2e6TLPg6nwmDUpl8\nu/s1wK9XwOQRACQLleShkjxMkoXKqjxsV8APHDhgdRfg0Odc+vq3zEhNo1n4rcwbNY+YiBi3zfOO\nlzLzk70M6hTHaw/0avI0qU0REHkECMlCJXmoJA+TZKGyKg+5Bu5vRfup+Ws2L7VP44RWw/t3/Z72\nMe5HnJ+5WEH2Rzu5LSaS9yZlEtHcdvtcQgghXLBdNYiP98614xtSfh6WT2Bu61ZsaVbNr27/FVlt\ns9w2r6iuJXvJTsora1j8eBZx0RFe75KleQQYyUIleagkD5NkobIqD9sV8C5duljzwrXVsOpxPtYv\n8pdbw5jScwoPdXnIbXNd15m5ei/7Tl7k9+P7072t+2VEb4ZleQQgyUIleagkD5NkobIqD9sV8K1b\nt1rzwn/7OdvP7OD/xLXijuQ7eD7zeY/NF3yTz9o9p3jxnm7c06utz7plWR4BSLJQSR4qycMkWais\nysN2BdwSOR9QuPvPPN8uhY6t0phz5xzCmrlfP3bDgSLmfnGY+/u1Y/pd8nENIYQQ17JdAY+I8P51\nZI+ObuLiF7OY0b4DYREtmDdqHi0i3M+Ze/jMZZ5ZsZve7VoxZ2xfr444d8XveQQwyUIleagkD5Nk\nobIqD9stZuJXF45Ss2gkP42PYWdEGIvvWUxmYqbb5iXlVTz4ziYqqh2smzH8utf2FkIIERJkMRNX\ncnNz/fNClWWw/DHmxISzrbnOK7e/4rF4V9c6mL5sF0WXKlk0OdNvxdtveQQByUIleagkD5NkobIq\nD9sV8PLyct+/iMMBn05jZcUxlkdH8njPx/lxlx97fMira/ezraCE2WP7MCC1te/7aPBLHkFCslBJ\nHirJwyRZqKzKw3YF3C++eZOtR9fzRps47ky5k+cyn/PYfMm2QpZtP8a0EWn8eECKnzophBAimNnu\nGviVK1e45ZZbvPZ819j/KUc/ncpj7VNJjO3Akv+11OOgtS35xUz+YAcjuibw/pSsm1rb+0b4PI8g\nIlmoJA+V5GGSLFQ+yMO6a+Capo3TNG20pmkz3dw/2/ia7YvX9+TkyZO+e/LTe7j42XSeTmlP88gW\nzLt7vsfiXXi+nOnLcukUH83bj/b3e/EGH+cRZCQLleShkjxMkoXKqjy8XsA1TcsA0HV9A1DqvN1A\ntqZp+UCBt1+/MadPn/bNE5edpXr5Y7xwWxtONNP4w8i3SYlxfzr8ckU1Uz/cia7D4ilZxESF+6Zf\njfBZHkFIslBJHirJwyRZqKzKwxdH4OOBUuP7AmC0izZP6bqebhT54FdTBSsnMzuiku0RGr8e+msy\nEl3tt9Spdeg8uyKPguJyFkzMoGN8tB87K4QQIhT4ooDHAiX1brdx0SbN0yl2X/L6nLW6Dp8/z/LS\nb1kZcwtP9HqCf+n8Lx4fMveLw3x16Cy/vr8nQztbuyiAzGlskixUkodK8jBJFiqr8rBkOVFd1+cA\naJo2RtO00Q2PxI1r49kAycnJbNy4EYCOHTvSokULvv32WwDi4uLo0aMHmzdvBiAsLIyhQ4eSl5fH\n5cuXARgwYADnzp3jxIkTACQkJBAeHn51/db4+Hi6dOlydS7biIgIBg8eTG5u7tWPBmRlZXHy5Mmr\np0m6dOlCWFgYhw4dot3xtRQdW8XspER639qLzB8yycnJYeDAgeTk5FBRUQHAoEGDKCws5K+7jvPe\nngoe6pvAmI6RV3+3pKQkkpOTcQ7Yi46OJiMjg+3bt1NVVQXAkCFDOHLkCMXFxQD07NmTyspK9RsT\nZAAAEYRJREFU8vPzAUhJSSEhIYHdu3cDEBMTQ//+/dmyZQu1tbUADBs2jIMHD1JSUnL1McePH+fo\n0aMApKam0rp1a/bs2QNAbGwsffr0YdOmTei6jqZpDB8+nH379lFaWneipV+/fly4cIFjx4557X1K\nT08nMjLSa+8TQGJiIh06dGDHjh0AREVFKe9TTU0NrVu3prCwkKKiIgC6d+9ObW0tR44csfR96t27\nN2VlZX59nxwOB23btg249wnMvyd/vk/h4eG0bds24N4nK/6eampquHTpUkC+T1b8PdXU1FBdXe21\n96lNG1fHvdfy+ih0Y4Dael3XN2iaNg5IcxZs4/5soETX9dXGEXipruuL3D2ft0ehb9y4kTvuuMM7\nT5b/d/6xYjwTU9qRGJvG0nuXEh3u/nR43vFSHlm4lQHtY1k6dTDhYdZ/is+reQQ5yUIleagkD5Nk\nofJBHpaNQl8JpBnfpwEbADRNizV+ttP5MyDduB18zudzcfUTPN0uifDIlswfNd9j8T5zsYLsj3Zy\nW0wkCyZlBkTxFkIIEby8XkV0Xc8F0DRtNHVH18455r6qd/8jxtF5fr37/SIxMfHmn6TiItXLH+WF\n1rdyqnkYvx/5Nsktkt03r64le8lOyitrWPx4FnHRgbMQgFfyCBGShUryUEkeJslCZVUetpvIpbKy\nksjIyBt/Akct+l8e5Telu/g4JprfDPsND3Z+0G1zXdd5ZkUe6/aeYuGkTJ+u7X0jbjqPECJZqCQP\nleRhkixUPshDFjNxxTng4oZ99TrLi7bwcUw0T/R+wmPxBnj363zW7jnFi/d0C7jiDV7II4RIFirJ\nQyV5mCQLlVV52K6A35S9H7Ml9z3mxLfhrvZ38cyAZzw2X3+giLe+PMwD/dox/a50P3VSCCGEHdiu\ngEdF3eAynSd3UfD5M7yYmEhabDpv3vEmYc3C3DY/dOYSz67YTZ/kVswZ1xdN8/80qU1xw3mEIMlC\nJXmoJA+TZKGyKg/bXQO/IZdOc3HxSB6LDafs1tYsv28F7Vq0c9u8pLyKB9/ZREW1g3UzhvttbW8h\nhBAhQa6Bu5KTk3N9D6iuoHrlYzwfDaebN+ftu//osXhX1zqYvmwXRZcqWTQ5M+CL93XnEcIkC5Xk\noZI8TJKFyqo8bFfAnTMENYmuo6/9N96o+Ac7osJ5ddhr9L+tv8eHvLp2P9sKSpg9tg8DUlvfZG99\n77ryCHGShUryUEkeJslCZVUetivg12XLPP5S+DmrWsbwk94/4YH0Bzw2X7L1KMu2H2PaiDR+PMD9\nSmRCCCHEzbLdNfAmf17vuy/Z/OkUprdNYET7kfxh5B9oprnf39nyfTGT/7SDEV0TeH9KliVre98I\n+TynSbJQSR4qycMkWajkc+B+UlhY2Hijc99RsOYpXkpMoEtsF968402PxbvwfDnT/5JLWnw0bz/a\nP2iKNzQxD5uQLFSSh0ryMEkWKqvysF0Bd66K49aVC5SuGM+MuBaER8Uyb9Q73Bp+q9vmlyuqmfph\n3RmCxY9nERMV7s3u+lyjediIZKGSPFSSh0myUFmVhyXLiQas2hqqP/7fPB9RzpnwaP509zySWiS5\nb+7QeXZFHgXF5Sx5chAd2rhfzEQIIYTwJtsdgXfv3t3tffqXv+K3l/aSExXJa8Neb3TE+dwvDvPV\nobP8+v6eDO0c7+2u+oWnPOxGslBJHirJwyRZqKzKw3YF3LnA+zV2L2XZwSV80rIFU/tM5f70+z0+\nz6e7T/DeN/k8NjiVybd38EFP/cNtHjYkWagkD5XkYZIsVFblYbsCfuTIkWt/eGw7mzbMYm6b1tzd\nfiRPD3ja43PsPnaBWZ/sY3CnOF57oFfATpPaFC7zsCnJQiV5qCQPk2ShsioP2xXwa1w8QcHqybyU\nEEeXVp15o5ER52cuVjBtyS4SW0ayYFIm4WESoRBCCP+zXfVJSqo3KK3qB0qXj+dnrZoTGdmKeaPf\n9TjivKK6luwlOymvrGHxlIHERUf4oce+peRhc5KFSvJQSR4myUJlVR62G4WenJxc942uU/3Zv/Ic\nZzkb3oI/jX7H44hzXdeZuXov+05eZNHkLLq1jfFTj33rah5CsmhA8lBJHibJQmVVHrY7AnfO6qb/\nz1x+W7SRnbdE8drw/6BfQj+Pj3v363zW7jnFi/d0Y0zPRH901S8sX+ktgEgWKslDJXmYJAuVVXnY\nroADcOhzlu76I5+0bMFTfaZyX9p9HpuvP1DEW18e5oF+7Zh+V7qfOimEEEK4Z7sC3qa2iI2f/4y3\n4lozKuUuZjQy4vzQmUs8u2I3fZJbMWdc36Aece5KdLRMPuMkWagkD5XkYZIsVFblYa/FTMrP8/0H\ndzEpRqd96858+M9/8ThoraS8igfmb6KqxsHaGcMDfm1vIYQQIUEWM2nowqdTmXFrDVFRrZg35j2P\nxbuqxsFPl+7i7OVKFk3JCtnivX37dqu7EDAkC5XkoZI8TJKFyqo8bFXAfxF7K2fDI/jj6AW0jW7r\nse1r6/az/R8lzBnbl/7tY/3UQ/+rqqqyugsBQ7JQSR4qycMkWaisysNWHyN7esgv6ZGzgb4JfT22\nW7L1KMu2H+NfR6TzLwPk4xJCCCECj72ugQM1NTU0b+5+v2XL98VM/tMORnRN4P0pWUG1tveNaCwP\nO5EsVJKHSvIwSRYqH+Qh18Bd8TRnbeH5cqb/JZe0+GjefrR/yBdvkDmN65MsVJKHSvIwSRYqmQvd\nT4qLi13+/HJFNT/5sO5If/HjWcREhfuzW5Zxl4cdSRYqyUMleZgkC5VVecg5EKDWofPMijz+UVzO\nkicH0aGNfMZRCCFEYLPdEXjPnj2v+dmcLw7x90NnefX+ngztHG9Br6zjKg+7kixUkodK8jBJFiqr\n8rBdAa+srFRuf7r7BAu/KWDi4FQmD+loTacs1DAPO5MsVJKHSvIwSRYqq/KwXQHPz8+/+v3uYxeY\n9ck+BneK49UHelnYK+vUz8PuJAuV5KGSPEyShcqqPGxXwJ3OXKxg2pJdJLaMZMGkTMLDbBuFEEKI\nIGS7qpWSkkJFdS3ZS3ZSXlnD4ikDiYuOsLpblklJSbG6CwFDslBJHirJwyRZqKzKw3aj0OPj43lp\n9V72nbzIoslZdGsbY3WXLJWQkGB1FwKGZKGSPFSSh0myUFmVh+2OwF9fvZ11e07x4j3dGNMz0eru\nWG737t1WdyFgSBYqyUMleZgkC5VVediqgH+5/wyrDlfyYP92TL8r3eruCCGEEDfMVgV8Td4pOseF\nM3tsXzQt9KdJbYqYGHtfQqhPslBJHirJwyRZqKzKw1aLmdQ6dC5dqaa1jQetCSGECHiymElDYc00\nDu7x3spmoWDLli1WdyFgSBYqyUMleZgkC5VVediqgAPU1tZa3YWAInmYJAuV5KGSPEyShcqqPGxX\nwIUQQohQYKtr4AAOh4NmzWS/xUnyMEkWKslDJXmYJAuVD/KQa+CuHDx40OouBBTJwyRZqCQPleRh\nkixUVuVhuwJeUlJidRcCiuRhkixUkodK8jBJFiqr8vBJAdc0bZymaaM1TZvZSDuP9wshhBDCNa8X\ncE3TMgB0Xd8AlDpvu2g3Ghjj7ddvTO/evf39kgFN8jBJFirJQyV5mCQLlVV5+OIIfDxQanxfAIz2\nwWvcsLKyMqu7EFAkD5NkoZI8VJKHSbJQWZWHLwp4LFD/gkCbhg00TcswjtD97ujRo1a8bMCSPEyS\nhUryUEkeJslCZVUeVg1ii7PodYUQQoiQ4Iv1wEsxC3QscL7+nU05+tY0LRvINm6WaZp22Iv9iweK\nvfh8wU7yMEkWKslDJXmYJAuVt/P4m67rP2qskS8K+Eogy/g+DdgAoGlarK7rpUCapmlp1BX5OKOg\n59Z/Al3XFwGLfNA3NE3bqet6VuMt7UHyMEkWKslDJXmYJAuVVXl4/RS6sxgbo8xL6xXnr4z7V+u6\nvtr4Way3X18IIYSwA18cgTuPoBv+LNNFG58cZQshhBChznYzsSE7DQ1JHibJQiV5qCQPk2ShsiSP\ngF/MRAghhBDXCvkjcHczwRn3NWnK11DRSBazja/Z7toIIYSo465u+LOuhHQBNwbSrXJzX5OmfA0V\nnrIwZGualk/d7HkhT9O0bOPfbDf3223nrrE8bLODZ7zvo2XbqNOEPGyzbTi5mwrc33UlpAu4EaK7\nghTQU756WyNZADyl63q6VTPk+ZPxx7fBGEiZZtyuf78dd+7c5mGwxQ6e8bs/bLz3GQ3fe5tuG27z\nMNhi22giv9aVkC7gjWh0ylebSbPRUUUa5h9WgXG7Plvt3NF4HmCTHTxd1zfouj7NuJnWcI4KbLZt\nNCEPsMm24dTIZGR+rSt2LuCiHl3X5xgbZRs3R2AhQ9f1RfU+6pgB7GzQxFY7d03IA+y1g+e8vjnN\nxV222jacPOQBNts2CKCpwO1cwD1O+WonxrXPccbN87g+Ags5xunAXDdHFbbjKQ877eBB3e8LTNM0\nTSabwnMedto2mjAVuF/riu0KeL0NcCVmobo65aud1MtiJ+bvn47rI7BQNFrX9Vkufm7XnTuXedhp\nB0/TtPrXeQsw12RwstW20Vgedto2DGnGIMZsjKnAwbq6EtIF3NiwsuptYGBO6epuyteQ1IQsHjHu\nyw/1LKDuPx7jqMK5Ddh6566RPOy0gzcatUAXgK23jcbysNO24WkqcEvqikzkImyn3kfqSqj7z+lh\nXdc3aJq2yznlr7GHXUDdwJ2QnnXqOvIooS6POdb11reMwvSIcTPTOYDLxttGU/MI+W0jEEkBF0II\nIYJQSJ9CF0IIIUKVFHAhhBAiCEkBF8KLNE27oGnaQk3TVhn/Yo2fj9Y0baGXXsMrz2WMMHY5PaY3\nH++qXYPRy64eM9rIMaQ/liTEzfDJeuBC2FhJvYE+adSNTs00PjvqlRHL9Z9L07Rx9UbFNup621so\no94MYEIIF+QIXAgf0XW9ANjg6UjTC8b7uL1V0o0dICGEG1LAhfCtHGCg8zSycWp4lXF6OMP4t8u4\nna9pWprxb5WmaeudKzwZ7Zyn5h8wnms24Hy+WONrmtF+potFWpT2xo+dz7vLeI76r1O/H84VuK72\n3dXjjddxPmZVg9ePNdqtAh52F5jx3OuBGz69L4QdSAEXwrdKG9x+GHhD1/Vp9SZ5KDBOF88Cpum6\nXqDr+sO6ro9BnX/6EeAp4ASAMWvaTqNtKbCwXvsxDad8dNEeIM547Q1AVoPXGQesN/ox3k3flcdr\ndfNhrzQes1JTl5jMBhbquv4wdQX6GsZOwGjjNH+ssdOw3vg6s96OxMJ6OwzjjGvq2fVuX6i3U2OX\nObqFzUgBF8K3Mqg7CneaDbxsFCXnKWLn4hgbjPbOI+jZqFNTflyv8F7DKNhpxvO6LJAuOGfOOo85\ns5TzddKBMcaAuVhggYu+N3z8QMxr/bmoayan0/g4gOx6bUqN3ynO+LoaGG98v4u6HYYMzAlVphk5\nrKZuB+QR6nYYZHIREZKkgAvhI0aRG19/0Jjz6Jq6Qu48WnZOVZkFFBhHjAXGEXP9la+aIsd4bm8M\nVNtF3dH0NF3Xx+i6nuei765e33nqfjTqzkt+vfs8reJV0OBriYv7AGKNMwG5xuWCq+2MzKcR4lN7\nCnuTUehCeFecccTqLMrKtV6jODuPSp0Lh6QZ14XTgFHG11WaptU/enVL07T1GKfegUXUnT4vaKx9\nY8+r6/oi4xT0NOouBeS46HvDx8wxHvMydTshD9e7Xr4I+Mr4vWJRizv12jyiaRrAG4310Xna3Ojr\nLE3T0nRdLzAK+lN43tkQIqjJVKpCWMgobtO89ZGpeqeUg+GjYo0yCvFC6naERmNei59N3RH3Kuou\nOzhP1+dgzuc+xthZyXWz6pwQQU0KuBAW8mYBN45GxxinuYUQIU4KuBBCCBGEZBCbEEIIEYSkgAsh\nhBBBSAq4EEIIEYSkgAshhBBBSAq4EEIIEYSkgAshhBBBSAq4EEIIEYT+PyLzg/rBZTnoAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bc0834a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seq 03\n",
    "compare_agg_acc(\n",
    "    \"../csv/kitti-tracking-sequence-0003-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-dynamic-mode-NO-direct-ref-with-fusion-weights-static-depth-result.csv\",\n",
    "    \"../csv/kitti-tracking-sequence-0003-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-NO-dynamic-NO-direct-ref-with-fusion-weights-static-depth-result.csv\",\n",
    "    \"dyn-vs-NO-dyn-tracking-03-full-dnet-sep-20m\",\n",
    "    \"Dynamic fusion\",\n",
    "    \"No Dynamic fusion\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KITTI 2015 stereo mean stats:\n",
      "Input:\t0.8975\n",
      "Dynamic fusion:\t0.9032\n",
      "No Dynamic fusion:\t0.9070\n"
     ]
    },
    {
     "data": {
      "image/png": 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0Du8Y6nVjoNjBh2Zj4JuAFNPjBCALQFGUICFEvSl4pwgh1iuKkna+4G0PKioq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\nIUKQTOBCCCFECJIJXAghhAhB/w8Ic8A9e9MF6QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bc1d9198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seq 04\n",
    "compare_agg_acc(\n",
    "    \"../csv/kitti-tracking-sequence-0004-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-dynamic-mode-NO-direct-ref-with-fusion-weights-static-depth-result.csv\",\n",
    "    \"../csv/kitti-tracking-sequence-0004-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-NO-dynamic-NO-direct-ref-with-fusion-weights-static-depth-result.csv\",\n",
    "    \"dyn-vs-NO-dyn-tracking-04-full-dnet-sep-20m\",\n",
    "    \"Dynamic fusion\",\n",
    "    \"No Dynamic fusion\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KITTI 2015 stereo mean stats:\n",
      "Input:\t0.8395\n",
      "Dynamic fusion:\t0.8422\n",
      "No Dynamic fusion:\t0.8355\n"
     ]
    },
    {
     "data": {
      "image/png": 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3WCxml+BSJA+dZGEkeRj1aR41R/ULz45uhJYz1qlKY1NoTP0BO3ym8V71UDYX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\nSm8+QFqopIdKepikhcqqHraawNfsLuCfG09xWVQYs8b1J6iZ712cRAg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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bc5e6dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seq 05\n",
    "compare_agg_acc(\n",
    "    \"../csv/kitti-tracking-sequence-0005-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-dynamic-mode-NO-direct-ref-with-fusion-weights-static-depth-result.csv\",\n",
    "    \"../csv/kitti-tracking-sequence-0005-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-NO-dynamic-NO-direct-ref-with-fusion-weights-static-depth-result.csv\",\n",
    "    \"dyn-vs-NO-dyn-tracking-05-full-dnet-sep-20m\",\n",
    "    \"Dynamic fusion\",\n",
    "    \"No Dynamic fusion\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KITTI 2015 stereo mean stats:\n",
      "Input:\t0.8766\n",
      "Dynamic fusion:\t0.8750\n",
      "No Dynamic fusion:\t0.8830\n"
     ]
    },
    {
     "data": {
      "image/png": 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R377bZU/Khh6jfDh8IRdH4+iFXDxt962zIT40xIUe8WGh29zNxkMbyT+cz75j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\nIUQAkgIuhBBCBKD/Byv6xFLf+F9xAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bc14ef98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seq 07\n",
    "compare_agg_acc(\n",
    "    \"../csv/kitti-tracking-sequence-0007-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-dynamic-mode-NO-direct-ref-with-fusion-weights-static-depth-result.csv\",\n",
    "    \"../csv/kitti-tracking-sequence-0007-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-NO-dynamic-NO-direct-ref-with-fusion-weights-static-depth-result.csv\",\n",
    "    \"dyn-vs-NO-dyn-tracking-07-full-dnet-sep-20m\",\n",
    "    \"Dynamic fusion\",\n",
    "    \"No Dynamic fusion\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KITTI 2015 stereo mean stats:\n",
      "Input:\t0.9015\n",
      "Dynamic fusion:\t0.8946\n",
      "No Dynamic fusion:\t0.8887\n"
     ]
    },
    {
     "data": {
      "image/png": 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aXMzRU3UMDwvkx7cm88DsMdalW5YKvtq9kUG+NX0v1Rp33flVtWxJtq2zjdJz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bc2c6cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seq 10\n",
    "compare_agg_acc(\n",
    "    \"../csv/kitti-tracking-sequence-0010-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-dynamic-mode-NO-direct-ref-with-fusion-weights-static-depth-result.csv\",\n",
    "    \"../csv/kitti-tracking-sequence-0010-tracking-dataset-offset-0-depth-precomputed-dispnet-voxelsize-0.0500-max-depth-m-20.00-NO-dynamic-NO-direct-ref-with-fusion-weights-static-depth-result.csv\",\n",
    "    \"dyn-vs-NO-dyn-tracking-10-full-dnet-sep-20m\",\n",
    "    \"Dynamic fusion\",\n",
    "    \"No Dynamic fusion\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ELAS results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KITTI 2015 stereo mean stats:\n",
      "Input:\t0.8523\n",
      "Dynamic fusion:\t0.8653\n",
      "No Dynamic fusion:\t0.8308\n"
     ]
    },
    {
     "data": {
      "image/png": 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BE7uDoFXflYBL4IIg+ChtV/VWdmWp/m9nI6BB+nK45Rm9lZ22DII80/KtrG1l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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bc50b588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sequence 00\n",
    "compare_agg_acc(\n",
    "    \"../csv/kitti-tracking-sequence-0000-tracking-dataset-offset-0-depth-precomputed-elas-voxelsize-0.0500-max-depth-m-20.00-dynamic-mode-NO-direct-ref-with-fusion-weights-static-depth-result.csv\",\n",
    "    \"../csv/kitti-tracking-sequence-0000-tracking-dataset-offset-0-depth-precomputed-elas-voxelsize-0.0500-max-depth-m-20.00-NO-dynamic-NO-direct-ref-with-fusion-weights-static-depth-result.csv\",\n",
    "    \"dyn-vs-NO-dyn-tracking-00-full-elas-sep-20m\",\n",
    "    \"Dynamic fusion\",\n",
    "    \"No Dynamic fusion\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Low-res comparison boxplots (and other plots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['input-elas-1.00', 'fusion-elas-1.00', 'input-dispnet-1.00', 'fusion-dispnet-1.00', 'input-elas-0.75', 'fusion-elas-0.75', 'input-dispnet-0.75', 'fusion-dispnet-0.75', 'input-elas-0.50', 'fusion-elas-0.50', 'input-dispnet-0.50', 'fusion-dispnet-0.50', 'input-elas-0.25', 'fusion-elas-0.25', 'input-dispnet-0.25', 'fusion-dispnet-0.25']\n",
      "100\\% &  0.0729 & 0.0724 0.0761 & 0.0807 \\\\\n",
      "75\\% &  0.0643 & 0.0689 0.0836 & 0.1031 \\\\\n",
      "50\\% &  0.0690 & 0.0807 0.1238 & 0.1602 \\\\\n",
      "25\\% &  0.1162 & 0.1624 0.5206 & 0.5989 \\\\\n"
     ]
    },
    {
     "data": {
      "image/png": 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kDV5nf39/MMrcD7/JZFLlcvlMWI/CtPdQKpVUq9WUyWRULBaVz+fPPDfu2gAAwNVh7n7Z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\nAMSIwA0AAADE6P8C6Q2dfuiOwG4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bc8c1ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def low_res_eval(root):\n",
    "    base = os.path.join(root, 'odo-06-{}-with-fusion-weights-static-depth-result-{:.2f}.csv')\n",
    "    res = {}\n",
    "    \n",
    "    scales = [1.00, 0.75, 0.50, 0.25]\n",
    "    metrics = ['input', 'fusion']\n",
    "    depths = ['elas', 'dispnet']\n",
    "    \n",
    "    for depth in depths:\n",
    "        for scale in scales:\n",
    "            fname = base.format(depth, scale)\n",
    "            df = pd.read_csv(fname)\n",
    "            augment(df)\n",
    "            for metric in metrics:\n",
    "                key = \"{}-{}-{:.2f}\".format(metric, depth, scale)\n",
    "\n",
    "                err = df['{}-error-3.00-kitti'.format(metric)]\n",
    "                tot = df['{}-total-3.00-kitti'.format(metric)]\n",
    "                mis = df['{}-missing-3.00-kitti'.format(metric)]\n",
    "                cor = df['{}-correct-3.00-kitti'.format(metric)]\n",
    "\n",
    "                error_perc = 1 - cor / (tot - mis)\n",
    "                res[key] = error_perc\n",
    "    \n",
    "    res_df = pd.DataFrame(res)\n",
    "    \n",
    "    columns = []\n",
    "    box_colors = []\n",
    "    ELAS_IN_COL = 'Purple'\n",
    "    DISPNET_IN_COL = 'Orange'\n",
    "    ELAS_FU_COL = 'RoyalBlue'\n",
    "    DISPNET_FU_COL = 'Black'\n",
    "    \n",
    "    box_positions = []\n",
    "    \n",
    "    idx = 0\n",
    "    for scale in scales:\n",
    "        for depth in depths:\n",
    "            for metric in metrics:\n",
    "                key = \"{}-{}-{:.2f}\".format(metric, depth, scale)\n",
    "                columns.append(key)\n",
    "                \n",
    "                if metric == 'input':\n",
    "                    if depth == 'elas':\n",
    "                        color = ELAS_IN_COL\n",
    "                    else:\n",
    "                        color = DISPNET_IN_COL\n",
    "                else:\n",
    "                    if depth == 'elas':\n",
    "                        color = ELAS_FU_COL\n",
    "                    else:\n",
    "                        color = DISPNET_FU_COL\n",
    "\n",
    "                box_colors.append(color)\n",
    "                box_positions.append(idx)\n",
    "                idx = idx + 0.75\n",
    "            \n",
    "        idx = idx + 1.0\n",
    "    \n",
    "    color = dict(boxes=box_colors)\n",
    "   \n",
    "    fig = plt.figure(figsize=(12, 8))\n",
    "    ax = fig.add_subplot(111)\n",
    "    (ax, boxplot) = res_df.boxplot(columns, showfliers=False, \n",
    "                                   return_type='both',\n",
    "                                   widths=0.50, \n",
    "                                   ax=ax, \n",
    "#                                    patch_artist=True,  # Enable fill\n",
    "                                   positions=box_positions)\n",
    "    print(columns)\n",
    "    \n",
    "    ax.set_ylim([0, 1.0])\n",
    "    ax.set_ylabel(\"Proportion of incorrect pixels\")\n",
    "    ax.set_xlabel(\"Input resolution\")\n",
    "    ax.grid('off')\n",
    "    ax.yaxis.grid(True, linestyle='-', which='major', color='lightgrey', alpha=0.75)\n",
    "    # Hide the gridlines behind plot objects\n",
    "    ax.set_axisbelow(True)\n",
    "    \n",
    "    for patch, color in zip(boxplot['medians'], box_colors):\n",
    "        patch.set_color(color)    \n",
    "        \n",
    "    for patch, color in zip(boxplot['boxes'], box_colors):\n",
    "        patch.set_color(color)\n",
    "    \n",
    "    # Ugly, but required since every box has two whiskers and two caps...\n",
    "    for idx, (whisker, cap) in enumerate(zip(boxplot['whiskers'], boxplot['caps'])):\n",
    "        cap.set_color(box_colors[idx%8 // 2])\n",
    "        whisker.set_color(box_colors[idx%8 // 2])   \n",
    "            \n",
    "            \n",
    "    bp_np = np.array(box_positions)\n",
    "    alt_ticks = bp_np[np.arange(len(bp_np)) % 4 == 0] + (0.75*3/2)\n",
    "    ax.set_xticks(alt_ticks)\n",
    "    ax.set_xticklabels([\"100\\\\%\", \"75\\\\%\", \"50\\\\%\", \"25\\\\%\"])\n",
    "    \n",
    "    # Dummies for showing the appropriate legend\n",
    "    ax.plot([0.0], [-1000], label=\"ELAS input\", color=ELAS_IN_COL)\n",
    "    ax.plot([0.0], [-1000], label=\"ELAS fused\", color=ELAS_FU_COL)\n",
    "    ax.plot([0.0], [-1000], label=\"DispNet input\", color=DISPNET_IN_COL)\n",
    "    ax.plot([0.0], [-1000], label=\"DispNet fused\", color=DISPNET_FU_COL)\n",
    "    ax.legend()\n",
    "    \n",
    "    for scale in scales:\n",
    "        print(\"{}\\\\% & \".format(int(scale * 100.0)), end='')\n",
    "        for depth in depths:\n",
    "            for idx, metric in enumerate(metrics):\n",
    "                key = \"{}-{}-{:.2f}\".format(metric, depth, scale)\n",
    "                print(\" {:.4f}\".format(res_df[key].mean()), end='')\n",
    "                if idx < len(metrics) - 1:\n",
    "                    print(\" &\", end='')\n",
    "        \n",
    "                \n",
    "        print(r\" \\\\\")\n",
    "\n",
    "    \n",
    "    fig_fname = \"reduced-resolution-result\"\n",
    "    plt.savefig('../fig/{}.eps'.format(fig_fname))\n",
    "    plt.savefig('../fig/{}.png'.format(fig_fname))\n",
    "\n",
    "# low_res_eval('../csv/ablation-res-bad')\n",
    "low_res_eval('../csv/ablation-res-correct')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f86bcec2d30>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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AcAaETwA9ijFGFw9L0xtfv1D/dfUIrd1zQJ/6zUL9xwtrVFnf7HR5AAAAOAXCJ4Aeyet2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\nAABsR/gEAAAAANiO8AkAAAAAsN3/B2kHF/fLvUOPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bcec26d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "x = np.arange(5, 35, 0.1)\n",
    "y =  100 / (x) \n",
    "xmax = 10\n",
    "y[y < 1] = 1\n",
    "y[y > xmax] = xmax\n",
    "\n",
    "plt.plot(x, y)\n",
    "plt.ylim(0 , xmax * 1.1)\n",
    "plt.axhline(1)\n",
    "plt.axvline(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
